Jobs ML / AI

We are looking for an AI/ML Research Scientist.

Requirements:

• PhD with 1+ years of research experience in Electrical Engineering, Computer Science, Information Systems, or a STEM-related field
• 1+ years of experience with modern machine learning architectures including Transformers, Graph Neural Networks, and Diffusion models
• Strong coding skills in Python and C++
• Experience with machine learning frameworks like PyTorch
• At least 1 publication in top machine learning conferences (NeurIPS, ICML, ICLR, etc.)
• Experience with deep learning on graphs (general graphs, circuit graphs, molecular graphs, or trees)
• Familiarity with classical search and optimization techniques (Genetic Algorithms, Monte-Carlo Tree Search, Dijkstra's Algorithm, etc.)
• Experience with EDA tools, either commercial or open-source
• Publications in top-tier venues focusing on AI for chip design or AI for materials science. 

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

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

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.

🚀 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

We are looking for an AI Product Engineer who can turn business problems into working digital solutions using modern AI tools, LLMs, automation platforms, and AI-assisted development workflows.

This is not a traditional Backend, Frontend, or Machine Learning Engineer position.

The role is designed for a technical AI generalist who can quickly understand a business need, validate an idea, build a functional prototype or MVP, and collaborate with specialized engineers and designers to bring the solution to production quality.

What you will do

• Analyze business processes and identify opportunities for AI automation and optimization.
• Turn business requirements into functional prototypes, MVPs, and internal tools.
• Build AI-powered web applications, dashboards, workflows, integrations, and product features.
• Use AI-assisted development tools such as Claude Code, Cursor, Codex, OpenAI, Anthropic, and similar platforms.
• Design and implement solutions using LLM APIs, AI agents, automation tools, and third-party services.
• Build end-to-end proof-of-concept solutions across different technologies when needed.
• Evaluate whether a task should be solved with custom development, AI agents, automation platforms, or a combination of approaches.
• Optimize prompts, context usage, token consumption, model selection, and AI-related costs.
• Test hypotheses quickly and validate whether a solution creates real business value.
• Prepare prototypes for further review and refinement by specialized developers, designers, or other domain experts.
• Document solution logic, limitations, risks, and recommendations for further development.

• Collaborate closely with product managers, engineers, designers, and company leadership.

   

What this role is about

The main goal of this position is not to develop one narrow technical area in depth.

The goal is to use AI as a force multiplier to:

• reduce development time;
• automate repetitive work;
• lower operational costs;
• validate product ideas faster;
• create tools that improve business performance;
• build functional solutions before involving a full specialized team.

You should be comfortable moving between different types of tasks and technologies. One project may involve an internal HR automation tool, another may be a sales workflow, an AI assistant, a web platform, or a mobile application prototype.

You are not expected to replace senior specialists in every area. You are expected to build a strong functional foundation that can later be reviewed, improved, and productionized by domain experts.

Requirements

• Strong technical foundation in software development, computer science, or a related field.
• Commercial software development experience with Python, JavaScript, TypeScript, or similar technologies.
• Practical experience building complete applications, prototypes, integrations, or automation solutions.
• Confident use of AI coding tools in real development workflows.
• Hands-on experience with OpenAI, Anthropic, Gemini, or other LLM APIs.
• Understanding of prompt engineering, context management, token usage, and model limitations.
• Experience designing AI agent or multi-step automation workflows.
• Ability to translate an unclear business problem into a structured technical solution.
• Ability to work across unfamiliar technologies and quickly learn new tools.
• Understanding of APIs, databases, authentication, integrations, and basic system architecture.
• Strong problem-solving skills and a product-oriented mindset.
• Ability to balance development speed with code quality, security, and maintainability.

• English level sufficient for working with technical documentation and participating in project communication.

   

Nice to have

• Experience with RAG systems, embeddings, vector databases, and document processing.
• Experience building AI agents and multi-agent systems.
• Experience with n8n, Make, Zapier, or other workflow automation tools.
• Startup, product discovery, hackathon, or MVP development experience.
• Experience working directly with business stakeholders or clients.
• Understanding of AI solution economics, including token costs, infrastructure costs, and expected business value.

The most important criterion is not the number of AI tools listed in your CV. We are looking for someone who can demonstrate how AI helped them deliver useful, functional solutions significantly faster and create measurable value for a business

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

Mirko Solutions is an international IT company specializing in web and mobile development for clients across the US, Canada, EU, and Australia. We are growing rapidly and are looking for a Middle\Senior AI Engineer to join our team and drive complex, high-impact projects.

Job Requirements:

• 3+ years of experience deploying machine learning models (classical ML and/or LLMs) in cloud environments (AWS/GCP/Azure) using containers and Kubernetes
• Hands-on experience with MLOps tools such as MLflow, Kubeflow, SageMaker, Vertex AI, Feast, or equivalent
• Proven experience building robust data/feature pipelines and implementing monitoring for model drift and performance
• Strong Software Engineering fundamentals: version control, automated testing, CI/CD, security, and cost-aware development
• Excellent collaboration and communication skills in  English 
• Ownership mindset, curiosity, and a bias for action in a fast-paced environment

Responsibilities:

• Design, implement, and maintain end-to-end ML pipelines for both classical ML and LLM-based solutions
• Deploy and manage models in cloud environments (AWS / GCP / Azure) using containers and Kubernetes
• Apply MLOps best practices for reproducibility, automation, and monitoring
• Build reliable data/feature pipelines and implement real-time monitoring for model drift, latency, and performance tracking
• Actively contribute to engineering excellence through CI/CD, testing, security, and cost optimization

Nice to have:

• Experience with deep learning frameworks such as PyTorch or TensorFlow
• Understanding of distributed systems (Spark, Ray)
• Experience working with feature stores and data governance

We provide the following for our employees:

• Competitive salary according to qualification
• Comfortable working environment
• Experienced colleagues
• Opportunity to work with interesting projects,
• Regular performance reviews
• Friendly atmosphere, care for the comfort of professionals