Jobs ML / AI

Cloudfresh ⛅️ is a Global Google Cloud Premier Partner, Zendesk Premier Partner, Asana Solutions Partner, GitLab Select Partner, Hubspot Platinum Partner, Okta Activate Partner, and Microsoft Partner.

Since 2017, we’ve been specializing in the implementation, migration, integration, audit, administration, support, and training for top-tier cloud solutions. Our products focus on cutting-edge cloud computing, advanced location and mapping, seamless collaboration from anywhere, unparalleled customer service, and innovative DevSecOps.

We're looking for an AI Architect to become our lead technical expert for client AI solutions and to help stand up our AI Competence Centre and internal AI Lab.

This is a client-facing technical role first, with a build-and-lead dimension. Expect roughly 60% solution architecture and pre-sales engineering for enterprise clients, and 40% establishing the AI Lab and mentoring engineers. You'll technically lead and raise the bar for the team - this is a technical leadership role, not a people-management line.
Reports to: Head of AI.

Requirements

• Experience & seniority. 5+ years in software, data, or ML engineering, including 3+ years delivering ML/AI in production and 2+ years in a client-facing architect or technical lead capacity.
• Cloud & AI infrastructure. Production-level experience on Google Cloud Platform, especially the Vertex AI suite, plus integrating third-party model providers such as Anthropic.
• GenAI engineering. Strong hands-on knowledge of RAG, agentic workflows, orchestration frameworks (LangChain, LlamaIndex), vector databases, and prompt/context engineering.
• Production & MLOps. Hands-on with the ML lifecycle, CI/CD for models, automated deployment, and cloud cost management - including right-sizing models (lighter vs. heavier) to control spend.
• Evaluation, guardrails & observability. Practical experience building LLM evaluations, guardrails, and monitoring for accuracy, safety, latency, and cost in production.
• AI governance & security. Familiarity with data governance, model risk, and EU regulation (EU AI Act, GDPR) as it applies to enterprise AI — we're primarily EU-based.
• Pre-sales & client experience. Proven experience presenting to enterprise clients, scoping technical projects, and translating complex AI concepts into clear business value.
• Technical leadership. Experience guiding engineers, reviewing architecture and code, and raising team capability.
• Languages. Native Ukrainian — required, as you'll run Ukrainian-language pre-sales — and strong English (B2+/C1 minimum).

• Mindset & agility. An intrapreneurial drive to build an AI Lab from scratch inside an established business, plus the agility to move smoothly between client pre-sales and deep technical architecture.

   

Responsibilities

• Solution architecture. Design and own end-to-end AI systems for strategic enterprise clients - reference architectures, data and retrieval pipelines, agentic layers, and integration with existing enterprise systems - with a clear path from prototype to scalable, secure, observable production.
• Pre-sales & consulting. Partner with sales to qualify and win AI opportunities: run technical discovery, scope solutions, size effort and cost, and translate AI into business value, ROI, and risk for C-level stakeholders.
• Technical leadership & mentorship. Set architecture and engineering standards, run design and code reviews, and level up engineers across the AI practice.
• AI Lab & R&D. Stand up and run the internal AI Lab - rapid client prototypes and cost-effective internal AI tooling - with a clear bar for what graduates into production.
• Partner collaboration. Work with our core AI partners (Google Cloud / Vertex AI, Anthropic) to build joint solutions, access early capabilities, and shape our technical roadmap.
• Governance, security & cost. Build guardrails, evaluation, and observability into every solution; keep architectures compliant (EU AI Act, GDPR) and cost-aware through right-sized model selection.

Would be a plus

• Google Cloud Professional Machine Learning Engineer and/or Professional Cloud Architect certification.
• Hands-on enablement or certification with Anthropic and Vertex AI generative-AI stacks.
• Direct experience mapping solutions to the EU AI Act risk framework.
• Fine-tuning, distillation, or model-optimization experience.
• Production experience with voice/multimodal AI (e.g., ElevenLabs) or AI-assisted engineering tooling (e.g., Cursor).
• Contributions to the open-source GenAI ecosystem.

Work conditions

• Flexible Work Format: Work remotely, allowing you to balance your professional and personal life efficiently.
• Training with Leading Cloud Products: Access in-depth training on cutting-edge cloud and AI solutions, enhancing your expertise and equipping you with the tools to succeed in an ever-evolving industry.
• International Collaboration: Work alongside A-players and seasoned professionals in the cloud industry.
• Global Exposure: Expand your expertise by engaging with international markets across the EMEA and CEE regions.
• Vibrant Team Environment: Be part of an innovative, dynamic team that fosters both personal and professional growth, creating opportunities for you to advance in your career.
  
  When applying to this position, you consent to the processing of your personal data by CLOUDFRESH for the purposes necessary to conduct the recruitment process, in accordance with Regulation (EU) 2016/679 of the European Parliament and of the Council of April 27, 2016 (GDPR).
  Additionally, you agree that CLOUDFRESH may process your personal data for future recruitment processes. You can withdraw this consent at any time.

Project Description:

Build reusable connectors, APIs, orchestration services, and integration capabilities that allow AI agents to securely interact with enterprise systems.

Responsibilities:

Develop reusable APIs, connectors, MCP tools, and service contracts.
Integrate agents with SharePoint, SAP, Databricks, SQL, and other enterprise systems.
Implement orchestration, state management, retries, exceptions, and workflow automation.
Apply authentication, authorization, RBAC, source restrictions, least privilege, and API governance.
Build reusable platform utilities and maintain reliability and scalability.

Mandatory Skills Description:

Bachelor's degree in Computer Science, Software Engineering, Information Systems, or a related field.
5+ years of enterprise integration, backend, API, or platform engineering experience.
Strong experience with REST APIs, microservices, authentication, and cloud-native services.
Experience integrating enterprise systems and implementing production-grade operational controls.
Working knowledge of Azure services, DevOps practices, and secure API design.

Nice-to-Have Skills Description:

Experience with SharePoint, SAP, Databricks, Azure SQL, and other enterprise-system integrations.
Experience building MCP servers or agent tool interfaces.
Experience with event-driven and long-running workflow architectures.
Azure Developer or Integration Services certification.

Languages:

English: B2 Upper Intermediate

Title: AI Engineer
Reports to: Principal AI Architect
Department: AI & Advisory
Location: Ukraine – Remote
Term: Full-Time

About Atlas Technica

Atlas Technica’s mission is to shoulder IT management, user support, and cybersecurity for our clients, who are hedge funds and other investment firms. Founded in 2016, we have grown year over year through our uncompromising focus on service.

We value ownership, execution, growth, intelligence, and camaraderie. We are looking for people who share our Core Values, thrive, and contribute to this environment while putting the customer first. At Atlas Technica, we offer a competitive salary, comprehensive benefits, and great perks to our global Team. We strive to maintain a professional yet friendly environment while promoting professional and career development for our Team Members. Join Atlas Technica now!

Position Overview

We are seeking an AI Engineer to join our growing AI & Advisory team in Ukraine. This is a hands-on engineering role focused on building, integrating, testing, and deploying AI-enabled applications and enterprise solutions for sophisticated client environments.

Working under the technical direction of a Principal or Senior AI Architect, the AI Engineer will translate approved architecture and security decisions into working software, integrations, deployment patterns, tests, and technical evidence. This role will build rapid proofs of concept while also helping move successful solutions toward secure, maintainable, and supportable production use.

The successful candidate will combine strong software engineering fundamentals with practical AI, Azure, integration, and security experience. This is not a research-only or prompt-engineering role; we are looking for an engineer who enjoys writing production-quality code, solving complex integration challenges, testing assumptions, and delivering reliable technical solutions.

Position Responsibilities:

• Build rapid proofs of concept and production-ready components for AI-enabled research, data access, document analysis, workflow automation, reconciliation, and internal application use cases
• Develop, review, refactor, test, and maintain application code using Python and other supported development technologies
• Build and integrate REST APIs, MCP components, connectors, webhooks, databases, enterprise applications, and data-integration services
• Connect AI-enabled workflows with Microsoft 365, SharePoint, Microsoft Graph, enterprise data stores, third-party platforms, and approved AI services
• Implement retrieval and data-processing solutions using embeddings, vector or hybrid search, metadata filtering, indexing, and access-aware patterns
• Deploy and operate applications within Microsoft Azure using approved hosting, identity, storage, database, networking, security, and deployment patterns
• Build repeatable development and release processes using Git, CI/CD, Docker, automated testing, and infrastructure automation
• Implement appropriate authentication, authorization, role-based access, secrets management, logging, monitoring, environment separation, and other security controls
• Test AI solutions for reliability, security, model failure, prompt injection, inappropriate data access, unsafe tool use, dependency failure, and other production-readiness risks
• Participate in architecture reviews, code reviews, client technical sessions, troubleshooting, production-readiness assessments, and technical handoffs
• Maintain clear implementation documentation, diagrams, runbooks, testing evidence, and technical decisions while proactively communicating blockers, scope changes, and delivery risks
• Contribute reusable code, deployment templates, testing approaches, connector patterns, and engineering standards to Atlas Technica’s Ukraine-based AI capability

Requirements:

• 5+ years of professional software engineering experience, including recent hands-on work with AI-enabled applications, cloud applications, data systems, or enterprise integrations
• Strong Python development skills with experience building APIs, services, scripts, applications, or data workflows
• Experience with at least one additional production development stack such as C#/.NET, TypeScript/Node.js, Java, or a comparable technology
• Hands-on Microsoft Azure experience across application hosting, identity, storage, databases, secrets management, logging, monitoring, and deployment
• Practical experience with LLM APIs, agentic workflows, retrieval-augmented generation (RAG), embeddings, structured outputs, prompt design, and model limitations
• Experience building or integrating APIs, connectors, MCP components, databases, data platforms, or enterprise workflow systems
• Strong understanding of authentication, authorization, role-based access control, least privilege, environment separation, and secure secrets management
• Experience with Git, CI/CD, Docker, automated testing, production troubleshooting, and modern software-development practices
• Ability to write clean, maintainable, and testable code and clearly explain technical and implementation decisions
• Strong problem-solving, ownership, documentation, and follow-through skills with the ability to work effectively in a remote and collaborative delivery environment
• English proficiency at B2 level or higher, with the ability to participate effectively in technical client discussions and produce clear written documentation

Desirable Qualities:

• Experience with Azure AI Foundry, Azure OpenAI, Azure AI Search, Microsoft Agent Framework, or comparable AI orchestration technologies
• Experience with Microsoft Graph, SharePoint Online, Microsoft Entra ID, Microsoft 365, Copilot Studio, or Power Platform
• Experience with technologies such as Azure Functions, Azure Container Apps, App Service, Azure SQL, PostgreSQL, Cosmos DB, Snowflake, or similar data and application platforms
• Experience developing evaluation datasets, regression tests, or other approaches for testing non-deterministic AI components
• Experience moving prototypes, internally developed applications, vendor solutions, or citizen-developed applications toward controlled production use
• Experience supporting financial services, regulated industries, enterprise clients, or other environments with strong security and compliance requirements
• Relevant Microsoft Azure, AI, software engineering, or cloud certifications
• Experience creating reusable engineering patterns, mentoring junior engineers, or contributing to engineering standards and technical knowledge sharing

Project Overview

Our client is looking for a complete team of 7 specialists with a strong academic and research background.

The team should consist of professionals who have experience with academic writing, scientific publications, and/or participation in Tier-1 academic conferences in the relevant field.

Team of 7 people

All team members must:

• Be based in Germany
• Good German
• Have experience in composing academic/scientific texts
• OR have experience participating in academic/scientific conferences, particularly Tier-1 conferences
• Have a minimum of 3 publications in the relevant field
• Strong research and analytical background
• Ability to work collaboratively as part of a dedicated team

Key requirements:

• Hands-on experience building AI agents and RAG pipelines
• Strong Python development skills and experience with REST APIs / FastAPI
• Experience with LangGraph, Agno, Google ADK or similar agentic frameworks
• Knowledge of LLMs, vector databases, embeddings, and AI evaluation
• Experience with AWS, Azure, or GCP; Vertex AI / Gemini is a plus
  Docker, Kubernetes/OpenShift, CI/CD
• Experience integrating AI solutions with APIs, external systems, and enterprise data
• Ability to build PoCs and scale them into production-ready AI solutions.

About the Product

We are developing a platform of AI analyst coworkers that deliver industry insights. The agents operate in secure, sandboxed environments and can use tools, communicate across channels, and complete complex tasks with human oversight.

About the Role

You will join a small engineering team building and operating the platform’s agentic AI capabilities. This is a hands-on role covering AI agents, system architecture, production reliability, feature design and secure deployment.

Tech Stack

- TypeScript, Node.js, and Bun

- LLM APIs, agent SDKs, and MCP

- SQL and SQLite

- GCP, including Cloud Run, Cloud Build, and Artifact Registry

- Terraform and CI/CD

- Event-driven messaging and asynchronous processing

- Structured logging, monitoring, and tracing

Key Responsibilities

- Design and build production AI agents that can plan, use tools, maintain context, and complete complex tasks.

- Design scalable systems for agent orchestration, messaging, task execution, permissions, and persistent state.

- Integrate LLM providers, external APIs, databases, and communication channels.

- Build secure, sandboxed environments for executing agent workloads.

- Develop evaluation, monitoring, and debugging tools for agent quality and reliability.

- Deploy and operate AI services on GCP.

- Own technical decisions and establish strong engineering, testing, and security practices.

Required Skills

- 7+ years of software engineering experience.

- Hands-on experience delivering production AI or agentic systems used by real users.

- Strong system design experience, particularly with distributed and event-driven systems.

- Strong backend development skills in TypeScript, Python, Go, Java, Rust, or a similar language.

- Experience with LLM APIs, tool calling, structured outputs, context management, and agent workflows.

- Experience with SQL databases, asynchronous processing, retries, idempotency, and failure handling.

- Production experience with GCP, Docker, CI/CD, and infrastructure as code.

- Strong understanding of application security, permissions, secrets, and workload isolation.

- Experience with observability, incident investigation, and production operations.

- Ability to work independently and communicate clearly in a fast-moving environment.

Nice to Have

- Experience with agent frameworks such as LangGraph, LangChain, CrewAI, OpenAI Agents SDK, Claude Agent SDK, or similar.

- Experience with Terraform, Kubernetes, vector search, RAG, or multi-agent systems.

- Experience with financial research or analyst workflows.

Who We Are

Adélie Cognition builds solutions for a private investment firms and offices powered by latest AI models.

We’re looking for an AI Research Scientist to join our AI&Research Unit, a team dedicated to pushing the boundaries of fundamental artificial intelligence and bridging the gap between academic advancements and real-world technology.

Our fundamental research stream focuses on on-device LLM efficiency, deep architectural explorations, and localized machine learning solutions for the macOS ecosystem. It helps uncover high-level utility and solve complex technical challenges directly on user hardware, establishing our technology as a generalized leader in the global market.

As an AI Research Scientist, you will take deep ownership of our fundamental research initiatives. You’ll define, test, and coordinate advanced optimization workflows and structural model modifications while driving strategic collaborations with global scientific entities.

If you’re excited to lay the foundation for on-device AI efficiency to become a global industry standard, we’d love to hear from you!

In this role, you will:

• Prepare fundamental research proposals within our specialized LLM efficiency and optimization streams.
• Investigate new directions in LLM optimization by surveying relevant academic publications, formulating hypotheses, and running deep-dive experiments.
• Collaborate closely with internal research scientists and external academic labs on joint research projects and scientific publications.
• Work alongside the applied research stream to surface, validate, and transition relevant research prototypes into downstream production environments.
• Contribute to a strong publication record at top-tier international AI conferences, 
• Take full ownership of your research niche, driving initiatives from ideation to implementation with a high degree of independence and autonomy.
• Take part in internal knowledge-sharing sessions and weekly paper clubs to consistently improve domain expertise.

Skills you’ll need to bring:

• Deep experience in Natural Language Processing (NLP) or a similar machine learning domain, gained through solid academic work, industry experience, or both.
• Strong theoretical and practical understanding of recent LLM optimization techniques (ex. quantization, KV-cache compression, speculative decoding, and distillation).
• Direct hands-on experience with parameter-efficient fine-tuning (including LoRA and other adapters) and mixture-of-experts (MoE) architectures.
• Advanced prototyping skills and a proven ability to implement complex algorithms and architectures directly from academic papers.
• Fluent programming capabilities in Python and modern frameworks such as PyTorch, JAX, or TensorFlow.
• Robust fundamental knowledge of linear algebra, probability theory, and mathematical statistics.
• Upper-intermediate level of English or higher for active scientific writing, international lab collaboration, and conference presentations.

As a plus:

• A track record of publishing original research at international research conferences in AI/ML/SE/HCI domains.
• Practical experience in performance engineering, including code profiling and low-level optimization (GPU, Metal, C++...).
• Hands-on experience training, running, or deploying localized LLM models on device frameworks like MLX.

🌍 Who we are:

Adaptiq is a technology hub specializing in building, scaling, and supporting R&D teams for high-end, fast-growing product companies in a wide range of industries.

🧩 About the Product:

We are building and scaling a mature, data-intensive B2B SaaS platform used by enterprise customers across multiple markets. The platform supports a diverse range of enterprise use cases across operational planning, information analysis, case management, and cross-team collaboration.

The product brings together large volumes of structured and unstructured data, domain-specific analytics, and configurable workflows to help users investigate complex cases, collaborate effectively, and make timely operational decisions.

🌟 About the Role:
We are looking for a Principal / Staff AI Engineer to design, build, and deliver reliable AI-powered capabilities as part of an established production platform.

You will work at the intersection of backend engineering, generative AI, and product development. Your responsibilities will cover the full delivery cycle - from exploring ideas and validating technical approaches to building production services, evaluating output quality, and improving solutions based on real-world usage.
This is a hands-on engineering role with significant ownership. You will have the opportunity to influence architectural decisions, engineering practices, and the broader adoption of AI across the product while working closely with engineering, product, and other cross-functional teams.
 

📈 Key Responsibilities:

• Design, build, and deploy production-grade autonomous agent systems that solve open-ended, complex, and multi-step tasks for real users.
• Build the core agent platform and harness, including planning, tool use, agent orchestration, context management, memory, and reusable agent skills.
• Develop agents that can independently determine their next steps, adapt based on intermediate results, and maintain direction during long-running tasks.
• Design human-in-the-loop mechanisms that define when agents can act independently and when human guidance, review, or approval is required.
• Develop methods and tooling to validate AI-generated results, evaluate agent behavior, diagnose failures, monitor production performance, and improve reliability over time.
• Build scalable Python backend services and integrate agent capabilities with distributed production systems, taking new functionality from experimentation to reliable real-world use.
• Own key architectural and technical decisions, establish AI engineering standards, and introduce new approaches that advance the team’s agent platform and engineering expertise.
  
   

💻 Required Competence and Skills:

• 7+ years of software engineering experience, including at least 1.5 years of hands-on experience building and shipping LLM-based products to production.
• Proven experience building autonomous AI agent systems used by real users at meaningful scale and capable of handling complex, open-ended, multi-step tasks.
• Hands-on experience with AI agent frameworks such as LangChain, LangGraph, Deep Agents, or similar. Experience selecting frameworks, identifying their limitations, and building custom components where needed.
• Strong backend engineering skills in Python or another language such as Golang, Java, or Rust, with the willingness to work primarily with Python.
• Experience with context and memory management, agent orchestration, and human-in-the-loop systems.
• Experience taking AI products from experimentation to reliable production use, including cloud deployment, system integration, AI quality validation, monitoring, and agent failure analysis.
• Strong end-to-end ownership, communication, and ability to make technical decisions, work independently, and adapt quickly in a high-velocity environment.
• Advanced English skills.

➕ Nice to Have:

• Experience with React.js and full-stack development, with the ability to contribute to frontend development when needed.
• Experience with vector databases and similarity search in production AI applications.

🚀 Why Us:

- We provide 20 days of vacation leave per calendar year (plus official national holidays of a country you are based in).

- We provide full accounting and legal support in all countries we operate.

- We utilize a fully remote work model with a powerful workstation and co-working space in case you need it.

About ChatRevenue.ai 

ChatRevenue.ai was founded by Ratmir Timashev (founder of Veeam) and Vlad Voskresensky (founder of Revenue Grid). 

We are building an agentic sales automation for post-CRM era. The AI-native product combines structured and unstructured data, LLM workflows, and agentic automation to help sales people to focus properly, work more efficiently and automate repetitive sales processes. 

The product is already being tested by early customers. We are growing the core engineering team and looking for people who want to work on real product systems with direct impact on architecture, quality, and speed of delivery. 

About the role 

We are looking for a Senior Generative AI Engineer with 5+ years of commercial Python experience and hands-on experience building production GenAI / RAG systems. 

This is a hands-on engineering role focused on: 

• Python backend  
• LLMs and RAG  
• AI Copilot / assistant workflows  

We are looking for someone who can take a problem, propose a practical solution, and move it to production. 

What you will do 

• Design, build, deploy, and maintain production GenAI / RAG pipelines  
• Integrate LLMs and agentic assistants into product workflows  
• Improve latency, reliability, scalability, and cost-efficiency of AI systems  
• Evaluate and implement new GenAI, multi-agent, and orchestration frameworks  
• Contribute to architecture and technical decisions across the platform  

What we are looking for 

• 5+ years of commercial Python experience 
• Strong backend experience with FastAPI, APIs, webhooks, Docker, Kubernetes, Git, and CI/CD 
• Commercial experience with LLMs in production 
• Strong hands-on experience with RAG, retrieval workflows, embeddings, and vector search 
• Experience with LangChain, LangGraph, LangSmith, or similar frameworks 
• Good knowledge of SQL, Pandas, ETL, data cleaning, and ingestion pipelines 
• English: B2 or higher 
• High ownership, independence, and product mindset 

Nice to have 

• Multi-agent LLM systems 
• Knowledge graphs, graph databases, or graph-based retrieval 
• Kafka, Azure Event Hub, or other event-driven systems 
• Azure cloud infrastructure 
• Production ML / NLP systems 
• Evaluation, tracing, observability, and optimization of AI pipelines 
• Graph algorithms or tools like NetworkX 

What we offer 

• Remote-first setup 
• Flexible schedule aligned with European time zones 
• A core engineering role in an AI-native product 
• Direct influence on technical decisions and architecture 
• Real ownership and visible product impact 

If you have strong Python backend fundamentals and real production GenAI / RAG experience, we’d love to talk. 

We are looking for a Senior/Lead AI Engineer specializing in Agentic AI and LLM-based systems to help design and implement production-grade AI capabilities.

The role combines strong backend engineering expertise with hands-on Generative AI experience. You will be responsible for building reliable AI services, integrating LLM-based solutions into enterprise applications, and establishing engineering practices required to operate AI features at scale.

You will work closely with AI engineers, backend developers, architects, and product teams to deliver secure, scalable, and maintainable AI solutions.

Requirements

• 5+ years of software engineering experience with strong backend/distributed systems background.
• 2+ years of experience building and operating production Generative AI / LLM-based systems.
• Strong Python engineering skills and experience developing production services.
• Hands-on experience with LLM application development and AI agent frameworks.
• Experience with frameworks such as LangGraph, Strands Agents, Semantic Kernel, or similar technologies.
• Experience designing AI workflows, tool integrations, and agent orchestration patterns.
• Strong understanding of LLM capabilities, limitations, model selection, and optimization strategies.
• Experience designing evaluation approaches for AI systems, including quality measurement and regression testing.
• Experience building secure AI solutions considering data privacy, access control, and responsible AI practices.
• Strong AWS experience, including services such as Amazon Bedrock.
• Experience with Infrastructure as Code (Terraform / CloudFormation).
• Experience designing event-driven architectures and scalable cloud services.

Job responsibilities

• Design and develop production-grade LLM-powered applications and agentic AI workflows.
• Build AI services using Python and modern AI frameworks.
• Develop reliable backend services supporting AI-driven functionality.
• Design and implement integrations between AI services and enterprise applications.
• Work with LLM platforms and evaluate model capabilities, performance, and cost trade-offs.
• Develop AI agent workflows including tool integrations, memory/session management, and orchestration.
• Establish best practices for prompt management, AI evaluation, testing, and continuous improvement.
• Design scalable solutions for AI output validation, monitoring, and production support.
• Collaborate with application teams to define API contracts and integration patterns.
• Support deployment and operation of AI workloads in cloud environments.

ABOUT OUR CLIENT

Our client is a leading enterprise data platform company building an open, high-performance data lakehouse for AI and analytical workloads. The platform combines an intelligent SQL query engine, an AI-ready semantic layer, and an open catalog built on Apache Iceberg — enabling Fortune 500 companies across finance, energy, manufacturing, and logistics to unify, query, and govern data at massive scale across cloud and on-premise sources.

ABOUT THE ROLE

We are looking for a Senior Software Engineer to build and operate production AI agent and search services on top of a large-scale data platform. This is hands-on work at the intersection of distributed backend engineering and applied AI: multi-agent workflows, tool-execution infrastructure, retrieval and relevance, and integrations with multiple LLM providers — all running as real, customer-facing production services with the reliability expectations that implies.

RESPONSIBILITIES

• Design and implement scalable multi-agent workflows (including conversation state), tool-execution infrastructure, and data-aware tools working with catalogs, schemas, lineage, SQL, jobs, semantic metadata, data preparation, and visualizations
• Build search and retrieval capabilities with OpenSearch or equivalent — indexing, relevance tuning, hybrid keyword + vector search
• Integrate multiple LLM/model providers with secure configuration and graceful degradation
• Develop APIs and event contracts across REST, streaming/SSE, gRPC, protobuf, and SDKs, including UI integrations
• Operate, monitor, and improve production services — service health, logs, metrics, traces, search-cluster health, latency, and error rates; participate in the team's production-support model
• Investigate incidents, drive root-cause analysis, and ship durable fixes
• Design for permissions, tool approvals, multi-tenancy, and customer-data governance
• Maintain dependencies, apply security fixes, and reduce tech debt; produce designs, tests, runbooks, and documentation; lead technical discussions and review code

REQUIRED QUALIFICATIONS

• B.S. or M.S. in Computer Science, Computer Engineering, or a related field — or equivalent practical experience
• 7+ years of relevant experience, or equivalent demonstrated senior-level impact
• Strong Java software engineering skills
• Strong grasp of concurrency, data structures, API design, testing, maintainability, and backward compatibility
• Solid background in distributed systems, microservices, cloud services, or data-intensive backends
• Hands-on experience with search technologies — OpenSearch, Elasticsearch, or Lucene — including vector search, embeddings, or relevance systems
• Experience building LLM applications, agent/tool workflows, RAG systems, or model-provider integrations
• Strong production instincts: diagnosing failures via logs, metrics, and traces; designing for fault tolerance, retries, cancellation, and recovery
• SQL and familiarity with data platform concepts: catalogs, metadata, schemas, governance, lineage
• Comfort leading ambiguous, cross-service work with clear stakeholder communication and strong written documentation
• Comfortable with AI-assisted development workflows — using modern AI tools productively while critically validating their output
• English Upper-Intermediate or higher (B2+) — daily written and verbal communication with a US-based engineering team
• Availability to work EU business hours shifted 2–3 hours later for daily overlap with US West Coast mornings

DESIRED SKILLS

• LangChain4j or similar agent frameworks
• Python for AI services, evaluation harnesses, or sandboxed execution
• React and TypeScript
• gRPC, protobuf, Quarkus, Kubernetes, MongoDB, KV stores, event-driven architectures
• Apache Arrow, Apache Iceberg, OpenLineage, semantic layers, or data-governance systems
• AI quality evaluation: LLM-as-judge, model benchmarking, production AI metrics
• OpenSearch operations: index lifecycle management, relevance tuning, incident response
• Open-source contributions

DETAILS

• Engagement: Long-term contract
• Location: Europe (EU / EEA / UK), remote
• Working hours: EU business hours, shifted 2–3 hours later for daily overlap with the US West Coast team

Company Overview

Robots & Pencils is an applied AI engineering firm building the next frontier of business architecture. We design and ship AI co-workers that integrate into enterprise operations and deliver measurable results for our clients. We're all in on AWS, combining deep UX capability with senior engineering talent to get AI into production fast and keep it there.
We’ve earned the trust of leaders across Consumer Products and Retail, Education, Energy, Financial Services, Healthcare, and Manufacturing and more, and earned a reputation as the nimble alternative to traditional global systems integrators. Founded in 2009, with delivery centers in Canada, the United States, Eastern Europe, and Latin America, we are smaller, faster, and more senior by design. Our teams average 15+ years of experience. We move fast, sweat the details, and build things that actually ship.
 

Position Overview

We’re looking for a Principal AI Engineer to define and drive the technical direction of our AI/ML systems. This role is ideal for a deeply experienced engineer who leads from the front — shaping architecture at the system level, solving the hardest problems, and raising the bar for how AI gets built and operated across the organization.

In this role, you will work on a cross-functional team, setting AI architecture standards, leading complex system design and implementation challenges, and solving integration problems that require both deep expertise and broad systems thinking. You’ll partner closely with leadership to define the AI roadmap and make high-stakes technical decisions that shape how our AI work scales across clients and over time.

Why This Role Matters

At Robots & Pencils, we design AI systems for a human world. Our name says it all. Robots and pencils means engineering paired with creativity, because every agent we ship has to work for real people in real workflows. That balance is baked into how we operate.

Every role here contributes directly to that mission. Here, you shape how AI systems integrate into enterprise operations, how teams move at real velocity, and how products create measurable impact for clients and the people they serve. We ship production-ready AI in 30 to 45 days. That pace demands people who take ownership, lead with craft, and care deeply about what they put their name on.

What You’ll Do

Craft & Delivery

• Define AI architecture and technical strategy, and lead implementation across the full lifecycle from design through production
• Build scalable ML platforms, pipelines, and workflow orchestration that support model development and event-driven, asynchronous operations at scale
• Architect and build LLM-powered systems, including prompt engineering, function/tool calling, multi-agent orchestration, RAG patterns, vector databases, embeddings, and streaming responses
• Design and develop APIs and backend services that integrate AI capabilities with enterprise systems and third-party platforms
• Lead model development, optimization, and the path from research to production, ensuring promising approaches translate into reliable, production-ready systems
• Ensure AI reliability, security, and scalability across deployed systems, including logging, monitoring, and debugging in production environments
• Bring an AI-forward mindset to your daily work, using tools like Claude, Cursor, and other modern AI assistants to ship higher-quality work at pace

Collaboration & Communication

• Co-define the AI roadmap with leadership, operating as a peer in strategic technical conversations
• Communicate complex technical concepts clearly to engineering and non-engineering stakeholders alike, translating depth into decisions others can act on
• Engage with product, engineering, and data teams to align AI work with broader business priorities

Leadership & Influence

• Define and champion AI engineering standards that shape how the organization builds and operates AI systems
• Mentor across the organization, shaping engineering culture and developing the next generation of technical leaders
• Own high-stakes architectural decisions that carry significant organizational and cross-engagement weight
• Drive technical vision — defining not just what gets built, but how AI engineering evolves at R&P over time

What You’ll Bring

• 9+ years of professional software engineering experience, with 5+ years focused on AI/ML systems in production and deep, hands-on expertise with generative AI development
• Expert software engineering background (Python or similar) with strong design sensibilities for distributed, scalable, and maintainable systems
• Deep experience designing distributed systems and event-driven architectures that underpin production AI workloads
• Deep expertise with cloud platforms, with mastery of AWS services and AWS GenAI offerings
• Proven track record architecting and shipping complex agentic systems in production across multiple teams or engagements
• Mastery of AI frameworks and orchestration tools, with clear judgment on when to adopt versus build
• Deep experience with evaluation frameworks and observability tools for LLM apps, including establishing these practices where they don't yet exist
• Deep expertise in AI safety, responsible AI principles, prompt injection defenses, PII handling, and AI governance and compliance in production
• Extensive experience designing RAG architectures: chunking strategies, embedding models, vector databases, and advanced retrieval techniques
• API and systems design experience, including architecting and integrating with internal and third-party services at scale
• Advanced cost optimization expertise: token economics, caching strategies, model routing, quantization
• Strong working knowledge of Docker, Kubernetes, and cloud-native AI architecture patterns
• Expertise in MLOps and AI infrastructure, including model versioning, monitoring, deployment automation, and reproducibility
• Demonstrable, day-to-day usage and expert command of AI-forward coding tools such as Claude Code and Cursor
• Track record of technical leadership and mentoring at an organizational level, with influence that extends beyond the immediate team
• Excellent problem-solving and stakeholder communication skills, with the ability to navigate the most ambiguous technical and business challenges with sound judgment

You’ll Do Well Here if You Are

• A doer. You see something broken and fix it. You'd rather move on clarity than wait for certainty.
• A fast learner who knows you don't know everything. The AI landscape changes weekly. You're senior enough to know better and curious enough to keep learning anyway.
• Direct in a way that makes the work better. You give honest feedback. You'd rather have the hard conversation than blow smoke.
• Obsessed with craft. You know genius is in the details. You ship exceptional, not perfect, and you don't put your name on work you wouldn't stand behind.
• Built for ownership. You honor commitments, admit mistakes fast, and back your teammates when a decision costs something. No handoffs, no finger-pointing.
• All in. You treat clients' businesses like your own. You take the work seriously without taking yourself seriously.
• Resourceful when the budget, timeline, or team is tight. Constraints don't slow you down. They sharpen you.
• Glad to be in the room with people who care as much as you do. Our teams average fifteen-plus years of experience. We hire people who push each other to do better work.

We Offer

• 20 days of paid vacation.
• 15 days of unpaid vacation.
• All official public holidays off.
• 5 paid sick days without a doctor's certificate.
• Medical insurance.
• Military draft deferment (reservation) support.
• Cooperation under a gig-contract.

Tech Stack:
• Python (5+ years)
• Strong working knowledge of SQL for data extraction and analysis (3+ years preferred)
• API integration with LLMs: OpenAI, Azure OpenAI, Claude
• ML/LLM architectures: RAG, embeddings, retrievers
• DevOps/MLOps: Docker, deployment of models on dedicated servers or cloud infrastructure
• Nice to have: FastAPI, vector databases (FAISS, Weaviate), prompt engineering
• Tools under consideration: LangChain or similar frameworks for rapid prototyping of LLM-based pipelines
 
Responsibilities:
• Design and build a production-grade LLM+RAG system for contextual data analysis
• Integrate external data sources and tools into the assistant’s knowledge base
• Set up and fine-tune LLMs on dedicated infrastructure or in the cloud
• Develop complex multi-step agents and orchestrated pipelines
• Build integrations with different platforms via API
• Ensure performance, scalability, and CI/CD automation of the ML systems
 
Requirements:
• Proven experience building real-world AI/ML products
• Deep understanding of the full machine learning lifecycle
• Experience working with popular LLM APIs and frameworks
• Ability to work independently and propose technical solutions
• Hands-on experience with LLM+RAG design patterns and prompt engineering
 
We Offer:
 

• A fully remote role with flexible engagement (full-time or contract) – you can also choose to work in the office or in a hybrid format if you prefer
• An opportunity to influence and shape the architecture and overall direction of the AI product
• Work at a large product company with a wide range of in-house developed solutions, many of which have been leading the market for over 15 years
• Access to diverse internal projects, enabling you to develop various skill sets and show your potential in different areas
• A competitive compensation package that reflects your experience and skills, with opportunities for review based on performance

About the Role

Our client is looking for a senior design technology leader who has successfully built and implemented AI-first design and development workflows in a real production environment.

This is not a traditional Head of Design role, nor is the client looking for someone whose AI experience is limited to experimenting with new tools or improving individual designer productivity.

The core challenge is much more ambitious: redesigning how product design and development work together so that teams can move dramatically faster from idea → prototype → validated design → production-ready implementation.

The client has already explored different AI-assisted design approaches and tools. What they need now is someone who has gone beyond experimentation and has actually built systems, workflows, and operating models that work end to end.

You should be equally comfortable discussing design systems, product design workflows, frontend architecture, AI-assisted development, prototyping, and organizational change.

Your mission will be to help the client build an AI-native design organization where AI is embedded into the way products are designed and shipped, not simply added as another productivity tool.

What You'll Do

• Define and implement an AI-first design and development workflow across the client's product teams.
• Dramatically shorten the path from product idea to prototype, validated design, developer-ready implementation, and where appropriate, production-ready code.
• Build workflows that connect design tools, AI systems, design systems, component libraries, and real application code.
• Establish practical approaches for AI-assisted ideation, prototyping, UX exploration, UI generation, design-to-code, iteration, and implementation.
• Design systems and processes that allow designers and engineers to collaborate effectively in an AI-native environment.
• Evaluate and integrate emerging AI design and development tools based on production value rather than hype.
• Partner closely with design, product, and engineering leaders to identify bottlenecks and redesign how teams work.
• Help establish or evolve design systems that can be reliably translated into real product components.
• Create standards and guardrails that allow teams to move faster without sacrificing product quality, consistency, or maintainability.
• Coach designers and engineers on AI-native workflows and help drive adoption across teams.
• Measure the impact of these workflows on design and development speed, iteration cycles, and product delivery.

What the Client Is Looking For

• Proven experience building and implementing AI-first design and development workflows in a real production environment.
• Strong understanding of modern product design workflows and how design connects to engineering.
• Hands-on experience integrating AI tools into workflows spanning ideation, prototyping, design, and software development.
• Strong understanding of design systems, component libraries, and the relationship between design artifacts and production code.
• Enough technical depth to work directly with frontend engineers and understand how design systems are implemented in real applications.
• Experience turning experimental AI workflows into repeatable systems that other designers and engineers can actually use.
• Strong product and systems thinking: you think beyond individual tools and optimize the entire product development workflow.
• Ability to distinguish genuinely useful AI capabilities from demos, prototypes, and hype.
• Experience driving meaningful workflow or organizational change across design and engineering teams.
• Strong written and spoken English.

What the Client Will Care About Most

The client will be particularly interested in concrete examples of systems you have already built.

For example:

• What did the design and development workflow look like before you changed it?
• What AI tools, infrastructure, design systems, or internal tooling did you introduce?
• How did design artifacts connect to real application code?
• How broadly was the workflow adopted by designers and engineers?
• What parts reached production?
• What measurable impact did it have on iteration speed or time from idea to production?

A compelling demo is useful. A workflow that a real team has successfully used to ship production software is much more important.

Nice to Have

• Strong frontend engineering background.
• Experience as a Design Engineer, Design Technologist, Product Engineer, or similar hybrid design/engineering role.
• Experience building internal AI tools, agents, plugins, or automation for designers and developers.
• Experience with modern AI coding and prototyping tools.
• Experience building token-based or code-connected design systems.
• Experience scaling design systems across multiple products or engineering teams.
• Experience leading or building AI-native product, design, or engineering teams.

What Success Looks Like

Within this role, success means changing how the client's teams actually build products.

Designers should be able to explore and validate ideas faster. Engineers should receive higher-quality, more implementation-ready outputs. Design systems should connect more reliably to production code. Repetitive handoffs and manual work should decrease significantly.

Most importantly, AI-assisted workflows should become a real, repeatable part of how the organization ships software, not a collection of isolated experiments.

The result should be a measurable reduction in the time required to move from an idea to a high-quality product experience in production.

ROLE OVERVIEW

We are looking for a Senior AI Software Engineer – Full Stack to design, build, and operate modern web applications and AI-powered solutions for the healthcare domain.

In this role, you will work across Front-end, Back-end, and DevOps, building AI-native features and agentic experiences using LLMs, retrieval, tool use, and structured workflows. You will also contribute to cloud-native services and help drive the adoption of modern AI engineering practices within the team.

CUSTOMER AND PROJECT

Our Customer is a leading provider of software solutions for the healthcare sector, operating across 19 countries with more than 9,000 employees.

The project focuses on developing and operating modern SaaS applications used in the healthcare domain. You will work on integrating AI-powered capabilities into products for clinicians and patients, while also contributing to cloud-native architecture, reliability, security, and developer productivity.

The role combines Java Full-Stack development, AI engineering, cloud-native technologies, and DevOps, with a strong focus on building practical AI features and agentic experiences that deliver real value to healthcare users.

TECHNOLOGY STACK

• React, Angular
• Java, Spring Boot
• Kafka, Terraform
• Kubernetes, Helm, ArgoCD
• Azure OpenAI, Anthropic SDK, MCP
• LLMs, AI agents, skills, workflows, AI pipelines and context engineering

RESPONSIBILITIES

• Design, develop, and operate modern web applications across Front-end and Back-end
• Develop scalable backend services using Java and Spring Boot
• Build AI-native features and agentic experiences using LLMs, retrieval, tool use, and structured workflows
• Develop AI agents, skills, and intelligent workflows that deliver practical value to healthcare users
• Design, evolve, and operate cloud-native services with a strong focus on reliability, security, and scalability
• Work with modern AI development tools and contribute to shared AI agents, skills, pipelines, and engineering workflows
• Collaborate closely with Product Management, UX, Architects, and other Engineering teams
• Contribute to engineering excellence and the adoption of modern AI engineering practices within the team

• Stay close to users and understand healthcare workflows to deliver outcome-driven solutions

   

REQUIREMENTS

• Several years of Senior-level Full-Stack development experience
• Strong commercial experience with Java and Spring Boot
• Solid Front-end expertise with React or Angular
• Experience with Kafka and Terraform
• Experience with cloud-native architectures and DevOps practices
• Hands-on experience with Kubernetes, Helm, and ArgoCD
• At least 1 year of hands-on commercial experience developing and integrating AI-powered features into software products
• Practical experience building AI agents, intelligent workflows, or automation solutions
• Experience with modern LLM ecosystems and AI platforms, such as Azure OpenAI, Anthropic SDK, or MCP
• Demonstrated practical use of AI tools in software engineering
• Experience with AI agents, skills, workflows, AI pipelines, or context engineering is a strong plus
• Degree in Computer Science, Software Engineering, or a comparable technical field
• Strong problem-solving, innovation, and proactive mindset

• Strong English communication skills

   

LANGUAGE REQUIREMENTS

English: B2 – Upper-Intermediate or above

🚀 Project Description

Facie is an early-stage AI product working with face image analysis.

We are looking for a strong Junior Computer Vision / ML Engineer to build a reliable mechanism for measuring facial geometry from photos.
 

The first challenge is straightforward to describe but non-trivial to solve: given a face photo, accurately measure numerical parameters of different facial features, such as eye dimensions, mouth dimensions, nose geometry, jaw proportions, distances between facial landmarks, and other geometric characteristics.
 

This is a greenfield CV problem.

You will not receive a predefined model or a detailed list of experiments to execute. We expect you to explore several possible approaches, quickly validate them, discard what does not work, and find a solution that is good enough to move into the product.
 

At phase 1 - our engineering philosophy is simple:

20% of effort that delivers 80% of the result is often better than spending weeks searching for theoretical perfection.
 

✅ You are:
 

• Confident with Python
• Understand core Machine Learning concepts
• Have practical experience with Computer Vision OR can demonstrate serious interest in the field
• Can work with existing open-source models and libraries
• Comfortable reading unfamiliar code and quickly running existing projects
• Understand basic model inference and API concepts
• Able to turn an unclear problem into several testable hypotheses
• Comfortable when the first approach does not work
• Able to explain how you would validate whether an experiment actually works
• Focused on finding a practical solution rather than endlessly optimizing one approach
• Comfortable using Git and working with an engineering team

Commercial Computer Vision experience is not required.
 

If your primary background is general ML or software engineering, we would still like to talk if you can demonstrate genuine interest in CV through things such as:

• university or online CV courses
• pet projects
• GitHub projects
• Kaggle
• research or thesis work
• reproduced papers
• personal experiments
   

➕ Will be a plus:
 

• OpenCV
• PyTorch or TensorFlow
• MediaPipe or other face landmark solutions
• Experience with face detection, landmarks or segmentation
• Understanding of 2D / 3D geometry
• Experience reading and implementing research papers
• Docker
• Model serving or inference pipelines
• Experience exposing ML functionality through an API
• Experience designing reproducible ML experiments
• Experience with Cursor, Claude Code, Codex or similar AI development tools
   

♟️ What We Expect from You:
 

• Experiment-first thinking - generate several possible approaches instead of betting everything on the first idea
• 80/20 mindset - optimize for time-to-market and find the simplest solution that provides enough accuracy for the product
• Ownership - do not wait for someone to define every next experiment for you
• Validation mindset - always ask how we know that a result is actually correct, especially when perfect ground truth does not exist
• Fast iteration - prototype, measure, learn, discard weak approaches and move forward
• Engineering judgment - understand when ML is needed and when deterministic geometry or conventional code is a better solution
• AI adoption - actively use modern AI coding tools when they help you iterate faster
• AI skepticism - understand and verify AI-generated code instead of blindly trusting it
• Clear communication - explain what you tried, what failed, what worked and what you recommend trying next
   

🔑 Responsibilities:
 

• Research different approaches for measuring facial geometry from images
• Build quick prototypes and experiments
• Evaluate existing Computer Vision models and open-source solutions
• Compare multiple approaches using measurable criteria
• Design practical validation methods when perfect ground truth is unavailable
• Analyze failure cases and identify why particular approaches fail
• Select solutions based on accuracy, complexity and time-to-market
• Build a reproducible facial geometry measurement pipeline
• Document experiments, conclusions and technical decisions
• Prepare successful solutions so they can be integrated by Software Engineers
• Ideally, help turn successful prototypes into an inference service or API
• Proactively suggest the next experiments instead of waiting for detailed tasks
   

🤖 AI-assisted Engineering
 

We actively use modern AI development tools.

You are welcome to use Cursor, Claude Code, Codex or similar tools to write code, explore libraries, integrate models and accelerate experiments.

We do not care whether every line of code was written manually.

We do care whether you understand:

• what you are testing
• why you selected a particular approach
• whether the result can be trusted
• how you validated it
• what the generated code actually does
• what you would try next if the approach fails
   

🍩 What We Offer:
 

• Part-time or Full-time depending on the candidate
• Remote work
• A real Computer Vision problem with direct impact on the product
• Significant ownership of the CV direction
• Freedom to experiment with different technologies and approaches
• Access to paid AI development tools where needed
• Direct communication with the founder and engineering team
• Opportunity to build a Computer Vision system from the greenfield stage into production
• Opportunity to grow from Strong Junior toward independently owning an ML / CV product area
   

🗣 Recruitment Process:
 

Intro Interview → Live Problem-Solving Session → Small Practical Experiment → Final Interview
 

💻 Live Problem-Solving Session (Optional)
 

This is not LeetCode and not a theoretical ML exam.

You will receive a previously unseen Computer Vision problem and will be asked to think aloud and visualize your approach using a whiteboard, Paint, FigJam or a similar tool.

We want to understand:

• how you decompose an unclear problem
• how many reasonable approaches you can generate
• what you would test first
• how you prioritize experiments
• how you validate results
• what you do when the first few approaches fail
• when you decide that a solution is good enough to ship
   

🧪 Practical Experiment (Optional)

Selected candidates will receive a small Computer Vision experiment.

You can use:

• AI coding tools
• Google
• documentation
• open-source models
• existing libraries
• research papers

We are not testing your ability to code without assistance.
 

We are testing your ability to turn an ambiguous CV problem into a working and validated solution quickly.
 

The expected output is a small working prototype plus a short explanation of:

• approaches considered
• approach selected
• why it was selected
• how the result was validated
• discovered limitations or failure cases
• what you would try next with one additional day