AI Engineer

Technologies we use

About the project

Your responsibilities

• Build and maintain production-grade LLM systems (including RAG, semantic search, embeddings, vector databases).
• Design and develop APIs and microservices in Python (e.g. FastAPI).
• Develop and maintain CI/CD pipelines for models (LLMOps/MLOps).
• Deploy and manage infrastructure in AWS (Lambda, ECS/EKS, S3, API Gateway, CloudWatch).
• Implement containerisation (Docker) and orchestration (Kubernetes).
• Monitor model quality (latency, drift, hallucinations, cost efficiency).
• Collaborate closely with Data Science, ML, Data Engineering and Product teams.

Our requirements

• Minimum 4-5 years of experience in an AI Engineer role or a similar position.
• Strong Python skills with experience building production APIs.
• Hands-on experience deploying LLM-based systems in production.
• Practical knowledge of RAG, embeddings, vector databases, and semantic search.
• Experience with AWS and scalable backend system design.
• Solid understanding of Docker, Kubernetes and CI/CD.
• Proven ability to move from PoC to stable production solutions.

Optional

• Experience with multimodal models (text, image, video).
• Familiarity with LLMOps/MLOps tooling and evaluation frameworks.
• Experience optimising inference performance and managing AI costs at scale.
• Background in product-led or data-driven organisations.

This is how we work on a project

Development opportunities we offer

What we offer

• Opportunity to work on advanced AI systems with real product impact.
• Strong influence on architecture and technology decisions.
• Collaboration with an experienced Data & AI team in an international environment.
• Flexible and remote-friendly working mode
• Health and wellbeing benefits

Benefits