Jobs ML / AI Engineer

Full-time
6months+
EU Only

About the Client
A major North American energy company — one of the largest players in energy infrastructure and distribution on the continent. The organization is investing in AI to modernize how it operates at scale, building LLM-based agentic systems to support decision-making and automation across a complex, data-rich environment. You'll join a team applying cutting-edge AI engineering to real production problems in a large, established enterprise.

Overview
You'll work on an AI/LLM platform built around agentic systems — designing, orchestrating, and hardening multi-agent workflows in production. This is a deeply hands-on engineering role focused on making LLM-based systems reliable, fast, and measurable, not just wiring up prompts.

Responsibilities

• Agentic systems: Build and operate multi-agent architectures — agent roles, triggers, handoffs, orchestration.
• RAG pipelines: Set up, tune, and fine-tune RAG systems end-to-end — chunking strategies, retrieval quality, iteration.
• Reliability engineering: Implement self-healing behavior — infinite-loop mitigation, graceful failures, clean handoffs between agents.
• Memory systems: Design and implement shared-memory concepts, with emphasis on fast memory.
• Evaluation & KPIs: Build evaluators of LLM performance, define KPIs, and automate evaluation (golden images, sample sizing).
• Performance & cost: Apply caching and token-optimization strategies to keep systems fast and economical.

Who You Are

• A hands-on builder who's actually shipped agentic/LLM systems, not just experimented with them
• Rigorous about reliability and measurement — you treat evals and failure modes as first-class work
• Comfortable owning ambiguous, fast-moving AI infrastructure end-to-end

Tech Stack You'll Work With
Core: Python, LLM tooling (llama / ollama), CUDA
AI systems: Multi-agent orchestration frameworks, RAG pipelines, LLM evaluation tooling
Concepts: Shared/fast memory, self-healing patterns, caching & token optimization, chunking strategies
Cloud: Azure (strong advantage)

Qualifications
Must-Have

• Strong hands-on experience with agentic systems (multi-agent orchestration, agent roles, triggers, handoffs)
• Strong hands-on experience building, tuning, and finetuning RAG pipelines (incl. chunking strategies)
• Experience with LLM performance evaluation — building evaluators, defining KPIs, automating them
• Self-healing / resilience implementation (loop mitigation, graceful failures, handoffs)
• Shared memory concepts and implementation, especially fast memory
• Caching and token-optimization strategies
• Python; llama/ollama; CUDA

Nice-to-Have

• Azure (products relevant to the above) — strong advantage
• Mistral — strong advantage
• Golden images and sample-size methodology for evaluation

The Role
Our standard delivery model splits work across a business analyst, a system analyst and team lead, developers, and testers. We are looking for a rare engineer who can own that entire cycle end to end — gathering requirements, shaping the technical solution and specification, building it across backend, frontend, and mobile, and testing it properly — using modern AI tooling as the force multiplier that makes this possible.

This is not a "prompt engineer" role and it is not a traditional full-stack role. It is a full-cycle builder who has genuinely internalized AI-assisted development: someone who uses coding agents, LLM APIs, and AI-driven testing daily to compress what used to take a team into what one strong, AI-fluent engineer can deliver — without sacrificing quality, security, or maintainability.

You will work directly with the CTO and product stakeholders, take an idea from a vague business need to a shipped, observable, production-grade feature, and set the standard for what AI-native engineering looks like at Billease.

WHAT YOU'LL DO:
Discovery & requirements
- Engage business stakeholders directly to elicit, clarify, and document business requirements.
- Turn ambiguous problems into clear, testable requirements — using AI to accelerate research, drafting, and gap analysis.

Solution design & specification
- Produce technical solutions and specifications: data models, API contracts (OpenAPI), service boundaries, sequence and architecture diagrams.
- Make sound architecture decisions aligned with our microservices, event-driven, and security-first principles, and document the trade-offs.

Build (backend / frontend / mobile)
- Implement backend services in Python and/or Golang on PostgreSQL, with appropriate messaging (Kafka) and caching.
- Build web frontends (Vue/Nuxt or React) and, where needed, mobile features (Android/Kotlin, iOS/Swift).
- Write reusable, testable, efficient code, using AI coding agents to move fast while keeping the codebase clean and reviewable.

Test & quality
- Design and execute meaningful test coverage — unit, integration, API, and end-to-end — leveraging AI-assisted test generation and review.
- Own quality end to end: you are the developer *and* the tester, and "done" means verified.

Ship & operate
- Deliver through our CI/CD pipelines and infrastructure as code.
- Build observability in at design time (metrics, logs, tracing) and support what you ship.

MUST-HAVE QUALIFICATIONS:
Mandatory: demonstrable, hands-on experience with a range of AI tools in real development work. This is the non-negotiable core of the role — we will ask you to show concrete examples of what you have built and how AI changed your workflow and output.

Real, current experience with AI development tooling, such as:
- AI coding agents / assistants (e.g. Claude Code, Cursor, Copilot, or similar) used to deliver production work, not just experiments.
- Working directly with LLM APIs (e.g. Anthropic, OpenAI) — prompt design, structured outputs, tool/function calling, and agentic workflows.
- AI-assisted testing, code review, documentation, and requirements/spec generation.

Full-cycle software engineering experience — you have personally taken features from requirement to production, not only implemented tickets handed to you.
- Backend proficiency in Python and/or Golang, with solid PostgreSQL and SQL skills.
- Frontend capability with a modern framework (Vue/Nuxt or React).
- Ability to write clear specifications and communicate effectively with both technical and non-technical stakeholders.
- Understanding of microservices, REST/API design, event-driven patterns, Git-based workflows, and CI/CD.
- Security- and quality-conscious mindset; comfortable owning testing and operability of your own work.
- Self-directed, high-ownership, and able to deliver under real deadlines.

NICE-TO-HAVE
- Fintech / financial services experience — lending, payments, digital banking, or regulatory-aware development (a strong plus).
- High-load / high-scale systems experience — performance tuning, partitioning, caching strategies, and designing for reliability under load.
- Mobile development experience (Android/Kotlin, iOS/Swift).
- Cloud-native and DevOps exposure: AWS, Kubernetes, Terraform/Ansible, Kafka, and observability tooling (Prometheus, Grafana).
- Experience integrating AI features into products (chatbots, voice, document/image processing, RAG, or automation).

HOW WE WORK
- Stack: AWS, Kubernetes, Python & Golang, PostgreSQL, Kafka, Vue/Nuxt & React, Android/iOS, Terraform/Ansible, GitLab CI/CD.
- Principles: clear and maintainable architecture, security-first, scalability and reliability, high observability and automation, cloud-native and microservices-oriented design, and a strong focus on performance, resilience, and operational excellence.
- AI-native by default: we expect AI to be part of how you think, design, build, and test — used deliberately and responsibly.

WHAT SUCCESS LOOKS LIKE:
Within your first few months, you independently take a real business need through the full cycle — requirements, spec, implementation across the relevant layers, tests, and a production release with observability — at a speed and quality that would traditionally have required a small team, and you help the rest of engineering raise its AI fluency along the way.

Are you passionate about building intelligent systems that can think, act, and adapt? At Sigma Software, we work on innovative AI-driven solutions that push the boundaries of technology. In this role, you will design, build, and optimize cutting-edge AI deployment systems — from autonomous agents to complex orchestration layers — for both greenfield products and established platforms.

We are seeking a highly skilled AI Deployment Engineer to lead the implementation of advanced AI solutions in operational environments. This role requires strong customer-facing presence, exceptional advisory skills, and the ability to design and deploy AI systems that deliver measurable organizational impact.

Requirements

• Proven experience in AI/ML deployment and system engineering.
• Strong proficiency in AI frameworks.
• Expertise in large language models, retrieval-augmented generation, and AI agents.

• Excellent communication and stakeholder management skills.

   

  Responsibilities

• Collaborate directly with customers, providing strategic guidance and technical expertise.
• Own the vision for AI operations implementation, building end-to-end workflows and taking them to production.
• Translate complex business challenges into actionable technical plans.
• Rapidly prototype AI solutions to demonstrate possibilities to clients.
• Engineer production-grade AI solutions with deep knowledge of AI tooling, architecture, and deployment.
• Manage stakeholder alignment, proactively escalating issues and requesting support when necessary.
• Design and build LLM workflows, RAG pipelines, agentic systems, and custom data pipelines.

We're building a product in the AdTech space, where AI is becoming a core part of the platform - from creative automation to advertising performance analytics. We're now forming a new direction: a team that will systematically bring AI solutions into the product and work cross-functionally with other engineering teams.

About the Role

We're looking for an AI Engineer who can become the core of the company's future AI team. This is not a research role, and it's not about training your own models it's about real, hands-on implementation of LLM-based solutions in the product, with a strong focus on product ownership and engineering quality.

You will:

• Implement AI features directly in the product (e.g., AI-powered regeneration of ad creatives, AI scoring of creative performance, analysis of how editors interact with ads)
• Work cross-functionally with other engineering teams, helping to systematize AI usage across the company
• Design and build LLM-based solutions: model API integration, prompt engineering, RAG, AI agents/tooling
• Proactively propose where and how AI can strengthen the product — rather than waiting for a fully defined spec
• Help lay the foundation for a dedicated AI team within the company over time
   

What Matters to Us

• A strong engineering background (backend/full stack), with production development experience — not just notebook experiments
• Hands-on experience integrating LLMs into real products (OpenAI, Anthropic, or similar APIs) — NOT experience training or fine-tuning your own models
• Understanding of prompt engineering, RAG, and building AI agents
• Product ownership: someone who generates their own ideas for how AI can move the product forward, and sees them through to implementation
• A genuine drive to implement solutions, not just conceptualize them

Who This Role Is For

• An engineer who is passionate about not only exploring or discussing AI approaches but also implementing AI solutions directly into production products.
• A professional with a strong engineering foundation who enjoys writing high-quality code and seeing its tangible impact on the product.
• A hands-on engineer who thrives on building and taking ownership rather than managing people. Someone who is eager to take end-to-end responsibility for designing, implementing, and continuously improving their product area.

About the Role:

We are looking for an experienced AI Transformation Lead to drive the adoption of Generative AI across our global organization. This is a strategic, cross-functional role focused on helping business and technology teams integrate AI into their daily workflows, improve productivity, and establish AI best practices.

This is not a Machine Learning or AI Engineering role. Instead, you will be responsible for identifying opportunities where AI can create business value, introducing modern AI tools and workflows, enabling teams through training and best practices, and embedding AI into everyday operations.

Key Responsibilities:

• Drive company-wide AI adoption initiatives across business and technology teams.
• Identify opportunities to improve business processes through AI-powered solutions.
• Evaluate, recommend, and implement modern AI tools and workflows.
• Develop AI best practices, prompting standards, governance, and usage guidelines.
• Deliver AI training sessions, workshops, and enablement programs.
• Partner with Functional Architects, Software Engineers, QA, DevOps, Project Managers, and Customer Support teams to maximize AI adoption.
• Continuously monitor emerging AI technologies and recommend innovative solutions.

• Measure AI adoption, business impact, and continuously optimize AI-enabled processes.

   

How You Will Make an Impact:

You will help different teams leverage AI to improve efficiency and quality, including:

Functional Architecture

• User Stories
• Functional Specifications
• Business Documentation
• User Manuals

Software Engineering

• AI-assisted code generation
• Code reviews
• Refactoring
• Documentation
• Architectural guidance

Quality Assurance

• Test case generation
• Regression test suites
• Test data generation
• Test automation improvements

DevOps

• Infrastructure scripting
• Deployment automation
• Monitoring and troubleshooting
• Operational runbooks

Project Management

• Meeting summaries
• Project planning
• Status reporting
• Action tracking

Customer Support

• Knowledge base creation
• Troubleshooting guides

• Customer communications

   

What We’re Looking For:

• Proven experience leading AI adoption, digital transformation, or process improvement initiatives.
• Strong understanding of Generative AI technologies and their practical business applications.
• Hands-on experience with AI productivity tools such as ChatGPT, Microsoft Copilot, GitHub Copilot, Claude, Gemini, Cursor, Perplexity, or similar platforms.
• Ability to identify automation opportunities and translate AI capabilities into practical business solutions.
• Experience designing AI-enabled workflows across multiple business functions.
• Excellent communication, facilitation, and stakeholder management skills.
• Strong understanding of Software Development Life Cycle (SDLC), Agile methodologies, and modern software engineering practices.

• Experience collaborating with cross-functional teams in technology-driven environments.

   

Nice to Have:

• Background in Software Engineering, Solution Architecture, Engineering Management, Technical Consulting, or Digital Transformation.
• Experience implementing enterprise AI initiatives.
• Experience developing AI governance frameworks and responsible AI practices.

• Familiarity with Microsoft 365, Atlassian, Google Workspace, or similar enterprise collaboration platforms.

   

If you are passionate about helping organizations transform the way they work through AI, we would love to hear from you.

At SPD Technology, we bring together a team of like-minded people who are driven by the desire to bring value through their work, united in their commitment to high performance and delivering custom, cutting-edge tech solutions that drive clients’ growth. We empower our people with a culture of excellence and enable them with the opportunity to uphold their accountability to contribute on each level. We value humanity and collaboration, encourage professional and personal growth, and foster a supportive and flexible work environment where everyone’s contribution is welcomed.

Now we are looking for a Software Development Engineer (MLOps) to join us as part of our team.

About the role

As a Senior ML Operations Engineer, you will support teams building and deploying AI-driven solutions, helping them overcome common challenges in the ML development, deployment, and support lifecycle. This role offers autonomy in decision-making, encouraging a proactive approach to solving complex issues and finding optimal solutions for scalable systems.

About the project

PitchBook — a platform for investment professionals. Our software provides access to data and the analytical tools to get answers fast and discover promising opportunities. Uncovers actionable insights and trends hidden within the financial data of more than three million companies. Users all over the world include large corporations, start-ups, venture capital and private equity firms, investment banks, and many others. Features of PitchBook: Advanced search / Discovery & insights / Company profiles / Workflow & efficiency / Financials and many more.

Team composition

1 Engineering Manager, 4 Senior ML Ops Engineers

Work Environment

The role offers a flexible work schedule, allowing you to adapt your working hours with the requirement to attend all team meetings. The team follows a Scrum-based Agile methodology.

As a qualified expert, You will

• Serve as a force multiplier for development teams by creating golden paths that remove roadblocks and improve ideation and innovation.
• Collaborate with other engineers, product managers, and internal stakeholders in an Agile environment.
• Provide mentorship, technical guidance, and perform code reviews for team members.
• Design and deliver on projects end-to-end with little to no guidance.
• Provide support to teams building and deploying AI applications by addressing common pain points in the MLDLC.
• Learn constantly and be passionate about discovering new tools, technologies, libraries, and frameworks (commercial and open source), that can be leveraged to improve PitchBook’s AI capabilities.
• Support the vision and values of the company through role modeling and encouraging desired behaviors.
• Participate in various cross-functional company initiatives and projects as requested.
• Contribute to strategic planning in a way that ensures the team is building exceptional products that bring real business value.
• Evaluate frameworks, vendors, and tools that can be used to optimize processes and costs with minimal guidance.

We’re looking for you if you have

• Degree in Computer Science, Information Systems, Machine Learning, or a similar field preferred (or equivalent practical experience).
• 5+ years of hands-on software development experience with Python (Java experience with strong Python proficiency also considered).
• 4+ years of experience designing and building distributed software systems and architectures.
• 3+ years of hands-on experience deploying and operating Machine Learning services in production.
• Experience supporting ML lifecycle operations including post-deployment monitoring and maintenance.
• Experience in cloud-native stack, with a practical understanding of containerization technologies such as Kubernetes and Docker.
• Demonstrated experience with SQL and NoSQL database design and implementation.
• Ability to decompose complex problems into iterative, well-defined solutions.
• Strong problem-solving abilities with focus on building scalable, efficient, and maintainable systems.
• Strong communication and collaboration skills, with the ability to engage effectively with internal customers across various cultures and regions.
• Ability to be a team player who can also work independently.
• Experience working across multiple development teams is a plus.

Desired Skills

• Experience with cloud platforms (AWS, Google Cloud Platform, or Azure).
• Proficiency in GitOps practices and CI/CD pipeline development and management.
• Observability and monitoring: Integration experience with observability tools (Prometheus, Grafana) and building instrumented, production-ready systems.
• LLM Infrastructure: Experience provisioning and managing Large Language Models through managed services (Azure OpenAI, Google Vertex AI, Amazon Bedrock).
• LLM Tooling: Hands-on experience with LLM gateways (LiteLLM) and agentic frameworks (LangGraph, LangSmith, or similar).
• Vector Systems: Practical experience with vector embedding models and vector databases (Pinecone, Weaviate, Milvus, pgvector).
• RAG Systems: Experience building Retrieval-Augmented Generation systems and evaluating both retrieval quality and generation performance.
• Cloud-native experience with services like Amazon SageMaker, Google Vertex AI, or Azure ML
• Familiarity with ML frameworks and tools: PyTorch, TensorFlow, scikit-learn
• Experience with data infrastructure: Redis, Elasticsearch, Apache Kafka.
• ML experiment tracking and model management: Weights & Biases, MLflow, KubeFlow.
• API development with FastAPI or similar frameworks.
• Java programming experience is a plus.

Interview Process

1. Pre-Screening with the recruiter
2. Tech Interview(ML Fundamentals, Software Engineering) + Live Coding (up to 1,5 hours)
3. ML System Design Interview (up to 60 min)
4. Interview with Engineering Manager (60 min)
5. Final Client Interview (60 min)

Project Description

This team is working with an AI chip company that develops specialized software-centric Machine Learning Systems-on-Chip (MLSoC) to enable high-performance, power-efficient "Physical AI" at the edge. Their technology is designed for robotics, autonomous vehicles, and smart cameras, allowing them to process complex computer vision and multimodal AI tasks locally with very low power consumption.

Job Description
The ideal candidate is an ML engineer with a strong background in autonomous systems and mobile robotics. The role requires someone who has spent meaningful time training ML models for complex autonomous platforms—focusing on perception, navigation, and control in GPS-denied, high-speed, and visually complex environments.

The candidate must have hands-on production experience with NVIDIA Isaac Sim and the Omniverse ecosystem, including building photorealistic simulation environments, running Omniverse Replicator synthetic data pipelines for perception, and closing the sim-to-real gap on real robotic hardware. Experience with reinforcement learning or imitation learning for navigation policies is required—specifically training agile navigation policies in simulation and validating them on hardware. A background from an autonomous robotics company, a research group, or an embodied AI team is a must.

On the ML side, the candidate will own the full perception model pipeline: training and fine-tuning visual-inertial odometry components, stereo depth estimation, and defect detection models on synthetic sensor data generated in Isaac Sim; quantizing those models to INT8/BF16 for deployment on the customer MLA; and profiling MLA utilization across navigation, inspection, and complex tracking use cases. The candidate will be the first to apply the custom Palette toolchain to a robotics AI stack—a genuinely novel workload requiring comfort navigating a custom MLA compiler with limited prior art. Strong PyTorch proficiency, familiarity with autonomous control stack integration, and a demonstrated track record of deploying perception models on real-world hardware are essential.

Job Responsibilities

We are seeking an exceptional Senior/Principal Engineer, Robotics Software to join our cutting-edge Robotics and AI software team. The ideal candidate brings deep, hands-on experience in autonomous mobile robots (AMRs), aerial robots. 

• Drive technical discussions relating to architecture, design, and development of software and hardware systems for AMRs, and/or humanoid robots.
• Build and deploy perception, localization, and navigation systems using Lidar-based and Visual SLAM techniques.
• Integrate deep learning models for object detection, motion planning, behavior prediction, and scene understanding.
• Contribute to or lead R&D efforts related to Generative AI and its applications in robotics.
• Collaborate with cross-functional teams including electrical engineers and AI scientists to deliver complete solutions.
• Mentor junior engineers and provide strategic input into long-term robotics and AI vision.

We are an international developer of IT products for medicine and related services. Our products are used by more than 5,000 doctors and more than 1,000 clinics in Europe.

The goal is to attract the largest possible audience to the main projects - clinics and doctors up to 3 million. As well as expanding unique traffic to 10 million per day until 2025. We are looking for AI Engineer / AI Architect with deep knowledge of Fundamental Mathematics, Probability Theory and Bayesian Statistics, Graph Algorithms, Mathematics of Transformers, Hybrid AI Approaches, Uncertainty Assessment.

Must-have skills are:
1. ML and Data Science (Core):
- Expert in NLP & LLM: Deep understanding of Transformers architecture (BERT, RoBERTa, GPT, T5, Llama/Mistral). Experience with Large Language Models (LLM) – not prompt engineering, but fine-tuning (PEFT, LoRA) of open-source models on specific datasets.
- RAG (Retrieval-Augmented Generation): Experience building RAG systems is required. The AI ​​must be able to search for information in different data sources.
- Named Entity Recognition (NER) & Information Extraction: Ability to extract specific entities from unstructured text.
- Tabular Data & Classic ML: Confident use of classical algorithms (XGBoost, CatBoost, LightGBM) for analyzing structured data and building probability scoring models.
- Frameworks: PyTorch (preferred) or TensorFlow/Keras. Proficiency in HuggingFace, Scikit-learn, Pandas, and NumPy.

2. MLOps:
- Experience wrapping models in APIs (FastAPI, Flask) with minimal latency. 

- Docker knowledge and experience working with local GPU instances are required.
- Experience working with vector databases (Pinecone, Milvus, Qdrant, ChromaDB) for quickly searching for similar cases or working with knowledge bases.

- ML Pipelines: Experience building model training and retraining pipelines (Airflow, MLflow, DVC).

3. MedTech and Security:
- Practical working experience with sensitive personal data and GDPR/DSGVO requirements.

- It is important to be able to deploy and optimize local models (Llama 3, Mistral, Mixtral) within a closed company environment to ensure data privacy.
- Skills in quantization and distillation of models to speed up work without significant loss of quality.

Nice-to-have skills:
- Experience working specifically with medical data (EHR, EMR; HL7, FHIR, ICD-10/11 standards). 

- Knowledge of Interpretability/Explainable AI (XAI) methods. The end user should understand why the AI ​​suggested a particular option (SHAP, LIME).

- Experience with multimodal models.

What we offer:
- Paid holiday for 28 working days.
- Transparent incentive system.

Working with us means:
- Unique technological and innovative projects.
- Gaining experience in modern western world practices in security, scalable architecture, etc.
- In-depth knowledge of telemedicine.
- Working with Big data, high load, distributed computing and data buses.
- Socially important projects.
- Opportunity to influence both technology and product solutions.
- Profitability and stability of a large European company.

🔹 100% remote | 🌎 Global team | ⏳ Full-time

NineTwoThree AI Studio is a premier product design, engineering, and marketing firm specializing in custom AI, web, and mobile applications for established brands and funded startups. We are based in Massachusetts but with an American and European staff and a strong, collaborative remote culture.

We’re a team that loves doing good work with great people. Our relatively small size keeps us fast and nimble. The wealth of knowledge, experience and talent paired with proven recipes and best practices allows us to find opportunities to help new products succeed.

With a portfolio of over 150 launched products over 13 years, NineTwoThree has garnered recognition as a top AI agency in the U.S., earning accolades such as inclusion in the Inc. 5000 list for four consecutive years and being named among the top 50 AI firms alongside industry leaders like Microsoft, NVIDIA, and IBM. We’ve built AI and ML tech for big brands like Consumer Reports, FanDuel, and Nara, as well as startups in legal tech, logistics, education, and more. 

Role Overview

As a Senior ML Engineer at NineTwoThree AI Studio, you will sit at the intersection of production-grade software engineering, advanced natural language processing, and client delivery. We build custom, high-impact AI systems for brands and startups across diverse industries (such as healthcare, logistics, and fintech).

Instead of siloed academic research, this role demands a product-minded builder. You will design, optimize, and deploy robust LLM applications, custom predictive analytics, and agentic workflows directly into our clients' software ecosystems, taking absolute ownership of features from prototype to production.

Technology Stack

• Core Frameworks & Arch: Transformer models, modern LLM APIs (Anthropic Claude, OpenAI, AWS Bedrock, etc.), Open-Source LLMs.
• Orchestration & Agentic Design: Experience designing LLM workflows, agentic systems, or retrieval pipelines using frameworks such as Langchain, LangGraph, LlamaIndex, or equivalent approaches.
• Data & Search: Vector databases (Pinecone, pgvector, Milvus, Qdrant, etc.), SQL, and data engineering pipelines.
• Traditional ML: Supervised and Unsupervised learning (Classification, Regression, Anomaly Detection).
• Cloud & Infrastructure: AWS (Lambda, SageMaker, Bedrock, EC2) and modern DevOps/retraining pipelines.
• Languages: Production-grade Python.

Responsibilities

• Architect & Build AI Features: Design and implement robust classical ML and generative AI solutions, striking the right balance between autonomous agentic architectures and deterministic pipelines.
• Evaluate: Design and maintain evaluation frameworks to measure AI quality, reliability, safety, and business impact before and after deployment.
• Integrate & Deploy: Partner closely with full-stack developers and DevOps to seamlessly integrate AI capabilities into client web and mobile applications using serverless architecture (e.g., AWS Lambda) or API endpoints.
• Optimize for Production: Refine prompts, system instructions, and chunking strategies to balance accuracy, latency, token consumption, and data privacy.
• Traditional Predictive Analytics: Clean and process unstructured or historical client data to train/fine-tune custom algorithms for specific business problems (such as forecasting, classification, or anomaly detection).
• Collaborate & Communicate: Actively participate in client discovery sessions, translate ambiguous business requirements into viable technical scopes, and demo prototypes directly to stakeholder teams.
• Maintain Engineering Excellence: Engage in constructive code reviews, implement rigorous validation patterns to test AI outputs, and contribute templates or runbooks to our internal AI knowledge base.

Technical Experience

• Proven Track Record: 3+ years of experience engineering software with a strong focus on machine learning and natural language processing.
• LLM & Generative AI Mastery: In-depth understanding of modern LLM architectures, context window mechanics, semantic search techniques, and the limitations of generative systems. Ability to identify when a deterministic solution is preferable to an LLM or agent-based solution.
• Production experience: Experience building and operating production AI systems, including monitoring, evaluation, debugging, and iterative improvement.
• Evaluation experience: Understanding of evaluation methodologies for LLM-based systems, including retrieval quality, hallucination detection, and task-specific performance measurement. Ability to reason about tradeoffs between quality, latency, cost, reliability, and engineering complexity.
• Python & SQL Proficiency: Exceptional Python coding skills and the ability to query, clean, and structure data efficiently.
• Cloud Infrastructure: Hands-on experience deploying ML or API services within cloud ecosystems, preferably AWS.
• Ownership: Comfortable taking ownership of ambiguous problems from initial discovery through production deployment and ongoing support.

Product & Team Capabilities

• Ambiguity to Execution: Ability to drop into a completely new industry vertical, understand its data constraints, and spin up a working proof-of-concept within a few weeks.
• The "Product Engineer" Mindset: Passion for seeing things ship and understanding why something is being built from a business value standpoint, not just what is being built.
• Communication: Fluent written and spoken English. Comfortable interacting with client stakeholders and breaking down technical workflows into clear concepts.
• Adaptability: Eagerness to experiment with and evaluate fast-emerging AI development tools, models, and frameworks.
• Education: Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field (or equivalent practical experience).

What We Offer

• Annual paid vacation: 20 days off per year during the first 3 years, increasing to 25 days in later years
• Paid sick leave, 10 national holidays, and 2 company days off
• Well-being budget
• Global health coverage 
• Maternity/paternity leave
• Reimbursement of expenses for professional development courses and certifications (up to 100% in agreement with Manager)
• Strong positive engineering culture, a tightly-knit team of professionals with a good sense of humor

What's The Process

We value your time and ours and make the process fast and easy. Our interview process takes the following steps: a short interview with the HR, 2nd technical interview with ML Engineer and CTO (optional), 3rd live-coding interview, Offer.

Location: London, UK (relocation is expected)

Employment: Full-time
 

We are looking for a Founding Engineer to join an early-stage AI startup building automation solutions for the Testing, Inspection, Certification, and Compliance (TICC) industry.
 

As one of the first technical hires, you will work directly with the CTO, helping to build the company's core AI platform while working closely with customers to automate real operational workflows.
 

Responsibilities:

• Design, develop, and deploy end-to-end software solutions.
• Work directly with customers to understand their business processes and identify automation opportunities.
• Build AI-powered solutions using LLMs, computer vision, workflow automation, and integrations with third-party systems.
• Develop features from idea to production with a strong focus on business impact.
• Integrate with legacy software, sensors, laboratory equipment, and external APIs.
• Contribute to the architecture of the core platform.
• Help shape engineering processes and grow the engineering team as the company scales.
   

Requirements:

• Strong software engineering background with the ability to work across different technologies and tech stacks.
• Experience building and shipping production software independently.
• Product mindset with the ability to solve business problems through technology.
• Excellent communication skills and ability to work directly with customers.
• Comfortable taking ownership and working in a fast-paced startup environment.
• Willingness to travel across the UK (2–3 days per week during customer onboarding phases).
   

Nice to Have:

• Production experience with AI/ML (LLMs, AI agents, Computer Vision, Data Science).
• Experience with cloud platforms (preferably GCP).
• Previous startup or Founding Engineer experience.
• Experience migrating legacy systems.
• Consulting or Forward-Deployed Engineering background.
• Experience in compliance, inspection, logistics, facilities management, or industrial software.
• Experience working with non-technical stakeholders.

We are looking for a Middle AI Engineer who will help us build, integrate, and scale AI-powered products that solve real business challenges.

This role is focused on creating production-ready AI solutions — from backend services and LLM integrations to RAG systems, AI agents, and workflow automation. You will work closely with Product Managers, developers, and business teams to transform ideas into reliable AI products that deliver measurable value.

What you will do:

• Develop production-ready AI services, backend components, APIs, and integrations for internal and external AI products.
• Design and implement AI solutions based on Large Language Models (LLMs), RAG architectures, AI agents, and workflow automation.
• Build and optimize Python-based services and backend infrastructure with a focus on scalability, reliability, and maintainability.
• Integrate and work with different types of AI models — including proprietary APIs (OpenAI, Anthropic, Gemini, etc.) and locally hosted models.
• Design and improve RAG pipelines: data processing, embeddings, vector search, retrieval optimization, knowledge bases, and AI response quality improvement.
• Develop AI agent systems and automation workflows, including integrations with external APIs, corporate systems, and internal tools.
• Conduct technical experiments with models, prompts, architectures, and inference parameters to identify the most effective solutions.
• Implement AI quality evaluation and monitoring approaches: analyze model performance, improve reliability, reduce errors, and optimize system efficiency.
• Collaborate with Product Managers and cross-functional teams to translate product requirements into technical solutions and scalable architectures.

• Ensure AI services are secure, stable, and production-ready by monitoring performance, resolving issues, and continuously improving systems.

   

What we expect from you

• 3+ years of commercial software development experience with strong Python expertise.
• Experience building backend services, APIs, and integrations using modern technologies.
• Strong understanding of software engineering principles: clean code, architecture patterns, testing, scalability, and maintainable solutions.
• Practical experience developing AI products based on Large Language Models (LLMs).
• Hands-on experience with RAG systems, embeddings, vector databases, retrieval optimization, and AI application development.
• Experience working with AI model integrations, including external APIs and/or locally deployed models.
• Understanding of AI agents, workflow automation, and approaches to building reliable AI systems.
• Experience with Docker, CI/CD, and production deployment processes.
• Ability to analyze AI system performance and improve quality, efficiency, and reliability.
• Upper-Intermediate English level (B2+) 

Will be a plus:

• Experience with fine-tuning or adapting AI models.
• Experience with MLOps / LLMOps practices.
• Knowledge of NLP approaches.
• Experience with cloud platforms and scalable infrastructure.

What we offer

• Competitive compensation up to $4,000, depending on your experience and expertise.
• Office-based work in Lviv with the possibility of a hybrid schedule.
• High level of technical ownership and the opportunity to solve challenging engineering problems.
• Professional development opportunities: company-sponsored courses, training, and access to necessary tools and resources.
• A team of experienced specialists who value expertise, initiative, and high-quality engineering.
• The opportunity to become one of the key engineers shaping the company’s AI direction.
• Relocation support for candidates from other cities, including assistance with moving and adaptation in Lviv.

Our client is the leader in the Early Childhood Education (ECE) sector, providing the most widely used developmental assessment and curriculum system in the US. 

They are early in shaping how AI changes the way they build and operate software. They have active initiatives in AI tooling cost-management and agentic development workflows, but their direction is still forming and will keep evolving as the field does. 

They are not looking for someone to execute a fixed plan, they are looking for an experienced practitioner who can validate the direction we are setting, bring expertise we do not have in-house, stay ahead of what is emerging, and help us adjust and grow as the landscape shifts. This person is hands-on enough to build and prove things directly, and senior enough that we trust their judgment on where to place bets.

What you will do

• Act as a hands-on technical partner across AI engineering initiatives, currently spanning tooling cost management, model routing and selection, and agentic development workflows.
• Pressure-test the approaches they are considering, tell them where they are wrong or where a better path exists, and back it with evidence rather than opinion.
• Bring outside expertise and patterns from how other organizations are solving these problems, so they are not learning everything the expensive way.
• Track what is emerging across models, tooling, frameworks, and techniques, and translate it into concrete recommendations on what they should adopt, defer, or ignore.
• Build and run proofs of concept to validate ideas quickly, then help them decide what to operationalize.
• Help mature their agentic development workflow so engineering teams can adopt it, including model routing and token efficiency.
• Partner with Infrastructure and Security so that what they build fits their architecture and meets their data privacy obligations.
• Help them build internal capability, so the expertise compounds with their team and does not leave when the engagement ends.

Required background

• 7+ years in software, platform, or ML engineering, with recent, direct experience building and operating LLM-backed systems in production.
• Demonstrated breadth across the current AI engineering stack: models and their tradeoffs, inference cost dynamics, gateways and routing, agentic workflows, and the AI coding tool landscape.
• A track record of forming a point of view on fast-moving technical decisions and being right often enough to be trusted with them.
• Strong hands-on engineering ability. This person builds, benchmarks, and ships, not just advises.
• Comfort operating with ambiguity and early-stage direction; able to bring structure rather than wait for it.

• Cloud and infrastructure fluency (AWS preferred, including Bedrock).

   

Strongly preferred

• Experience helping an organization adopt AI-first engineering practices, not just individual tool use.
• Hands-on model evaluation and routing design, and exposure to AI cost governance.
• Experience in regulated data environments. 
• A habit of staying current through real practice (building, writing, contributing) rather than headlines.

What this role is not

• Not a fixed-scope implementation seat. The work will change as their direction evolves, and they want someone who helps drive that change.
• Not a data science or model training role. The focus is applied AI engineering, integration, and enablement.
• Not a manager. Individual contributor, hands-on, with influence earned through expertise.

The quality of RLVR training data is one of the highest-leverage problems in AI right now. Bad environments, noisy rewards, and weak task specs silently degrade model capabilities — and most teams don't catch it until it's too late. At Arimlabs, we produce RL environments across software engineering, cybersecurity, SRE, finance, and policy — which means the surface area for subtle data quality failures is enormous, and the payoff for catching them is real. We're hiring someone to own that problem end-to-end.
 

What You'll Do

You'll lead data quality and experimentation strategy across our RLVR pipeline — not as a support function, but as the person whose judgment determines what's good enough to ship and what isn't.

• Set and enforce quality standards across RLVR environments produced by multiple domain teams — catch broken tests, weak specs, false positives, and reward hacking before they contaminate training.
• Design and run controlled SFT/RL/RLVR experiments to measure how dataset changes impact model performance. You decide what to test and how.
• Analyze trajectories, pass rates, reward distributions, task difficulty curves, and failure modes to figure out what's working and what isn't — then drive changes based on what you find.
• Partner with SPLs and domain teams to close the loop, translating experimental results into concrete data quality improvements.
• Shape our public evals, new data types, and external-facing research that raises the bar for the field.
• Publish blog posts, technical reports, and research writeups that share what we're learning.

What We're Looking For

• Strong quantitative instincts and hands-on familiarity with LLM training, RLHF/RLVR pipelines, or evaluation methodology.
• A genuine obsession with how data structure, selection, quality, and reward design drive model behavior — not just awareness, but the kind of curiosity that keeps you digging.
• Ability to design lightweight experiments, move fast, and extract signal from messy, ambiguous results.
• Comfort working across domains — you won't need deep expertise in cybersecurity or finance, but you will need to review environments built by specialists in those areas, ask the right questions, and spot when something doesn't hold up.
• Bias toward building and testing over theorizing. We care about what the experiments show, not what sounds elegant.
• Sharp attention to detail. The failure modes in RLVR data are subtle, and catching them is the job.

Who Tends to Thrive Here

• Undergrad or master's researchers with serious, hands-on research experience. A PhD is welcome but not required — what matters is whether you've done the work.
• People who've worked or interned at RL environment companies, AI safety orgs, benchmarking teams, or eval-focused groups (think METR, Artificial Analysis, Apollo Research, or similar).
• Early-career candidates who want to grow with a team that's still small enough for them to shape how we do things — and become central to our data quality and experimentation process long-term.

Deep AI/ML & Dev Background: A strong foundation in traditional machine learning, computer vision, and modern Generative AI/Agentic frameworks. You have a proven track record as a software engineer (Python, C++, etc.) who understands production-grade architecture.
Physical AI Familiarity: True passion for the intersection of software and the physical world. You understand how AI interacts with edge computing, robotics (ROS), IoT protocols, or industrial automation (PLCs).
Industry Awareness: You are genuinely obsessed with the state of the art. You know what models dropped last week, which hardware accelerators are trending, and how competitors are approaching physical automation.
The Proactive Ideator: You don't wait for a ticket to be assigned. You look at a client's industrial problem, generate three viable AI solutions, pick the best one, and start building the proof-of-concept.
Technical Communicator & Consultant: Excellent communication skills with the ability to translate complex AI models into clear business value for C-level clients during pre-sales, while maintaining deep technical credibility with engineering teams.

Job Responsibilities:

Engineering Unit Ideation & Execution: Generate, evaluate, and implement cutting-edge Physical AI use cases. You will own the technical roadmap for our prototypes, ensuring we are constantly pushing the boundaries of what AI can do in the physical realm.
Pre-Sales & Client Architecture: Partner with business development to review client requirements, architect end-to-end Physical AI solutions, and present the technical vision directly to customers. You will support the critical transition from a signed contract to a launched project.
Technical Review & Mentorship: Act as the ultimate technical authority for the Engineering Unit. You will review code, evaluate architecture diagrams, ensure high engineering standards, and guide junior developers as the team grows.
Internal Networking & Knowledge Sharing: Connect with technical leaders and capabilities across the company to leverage existing expertise. You will keep your finger on the pulse of global AI/ML advancements, ensuring our Engineering Unit always uses the latest, most efficient technologies.
Hands-on AI/ML Development: Maintain a strong development presence. You will design and implement the software glue, agentic workflows, and ML models that allow digital brains to interact with physical sensors, actuators, and industrial systems.

Department/Project Description:

The Vision
Generative AI is stepping out of the browser and into the physical world. We are building "Physical AI" - bridging digital agentic intelligence with real-world mechanical, robotic, and industrial systems. To prove this to our clients in manufacturing, energy, and mobility, we don't just build slide decks; we build functioning physical prototypes and architect scalable, real-world solutions. We are establishing a brand-new Physical AI Engineering Unit to target massive operational impact for Fortune 500 customers. We are not running isolated lab experiments; we are engineering the future of physical operations, and we need a technical anchor to help us build this foundation from the ground up.
The Role
You are the technical heartbeat and catalyst of our Physical AI EU. This is a highly dynamic, proactive role that demands a rare blend of deep technical mastery (AI/ML + software development) and entrepreneurial drive. You aren't just waiting for architectural blueprints to be handed to you; you are the one generating the ideas, reviewing the technical solutions, and bringing them to life.
You will naturally connect the dots across our company - communicating with internal engineering teams, staying ahead of the latest industry breakthroughs, and acting as the technical face of this EU during pre-sales activities.

Description

Customer is a US medical Company that is dedicated to transforming patient’s lives at scale by being the driving force to decentralize the delivery of safe care into their homes or other preferred sites of care.
Project provides health systems with all that is needed to safely care for patients, including the clinical protocols, reimbursement model, platform technology, and fulfillment of all the clinical services required in the home through partners. Clinicians and patients broadly prefer this model over traditional care; both clinical and financial outcomes are improved as compared to traditional care.

Requirements

Must have:

• Strong Python skills and solid Computer Science / software engineering fundamentals
• Strong hands-on experience with LLMs and generative AI: building, integrating, and shipping LLM-powered applications to production (not traditional ML/model training)
• Practical experience designing and building agentic workflows / AI agents (e.g., LangChain, LlamaIndex, Bot Framework, Claude/Bedrock agents, or similar)
• Experience with agent and LLM evaluation: measuring quality, grounding, and reliability of AI solutions
• REST APIs and integration of AI solutions with enterprise data sources (SharePoint, Confluence, Jira, etc.)
• Delivery-focused seniority: a mature engineer able to own solutions end-to-end and grow into a team lead role
• Excellent communication and collaboration skills: open-minded, comfortable in stakeholder meetings, works well in a remote setup

Nice to have:

• Microsoft ecosystem experience: Copilot Studio / Power Platform, M365, Azure OpenAI
• AWS Bedrock and Claude-based solutions (the client is actively rolling out Claude through Bedrock)
• Some data engineering experience (pipelines, data preparation for RAG/grounding — no need to be a guru)
• Experience in regulated environments (healthcare a strong plus)
• Azure Functions, CI/CD pipelines, DevOps practices
   

Job responsibilities

• Design, build, and productionize generative AI solutions and agentic workflows for company-wide use (engineering and non-engineering users)
• Contribute to the three major AI engines currently in development, including supporting and pairing with existing team members
• Build and run evaluations of AI agents: quality, grounding against private knowledge bases, and reliability
• Ground AI solutions in enterprise knowledge sources (SharePoint, Confluence, Jira) with appropriate access and security controls
• Write custom code (Python, APIs) to extend beyond low-code tool limitations where needed
• Assess and advise on platform/tooling choices (Copilot Studio vs. Claude/Bedrock vs. custom), including cost and licensing trade-offs
• Participate actively in stakeholder meetings; translate business needs into delivered AI solutions
• Grow into a team lead role: set engineering direction, mentor AI enthusiasts on the team, and enable the sponsor to step back into a strategic role