Senior AI Engineer
Indexed description
This is the job
You'll be working on the core engine of a production AI assistant, developing Agentic AI solutions using RAG and LLM orchestration to enable the assistant to move beyond answering questions and safely execute real customer requests.
This is you
- 3+ years building production Python services (async, API design)
- Experience designing and building REST APIs (and consuming them); comfortable with request/response modelling, auth, and versioning
- Hands-on experience building RAG / retrieval-augmented generation systems: not just calling an LLM API, but chunking, retrieval, and grounding answers in sources
- Experience with any vector database (e.g. Milvus, Pinecone, Weaviate, Qdrant, pgvector) and an understanding of embeddings and similarity search
- Worked with an LLM provider (OpenAI / Azure OpenAI / Anthropic / open models) in production
- A habit of evaluating output quality, you can explain how you measured whether a RAG system was good
- Writes automated tests as a matter of habit unit and integration testing with pytest (mocking, fixtures) Comfortable with GitHub in a team workflow, feature branches, pull requests, and code review
- LlamaIndex (or LangChain) framework experience
- Milvus specifically
- Azure OpenAI deployments
- LLM observability / tracing (Langfuse, LangSmith, OpenInference) FastAPI, Pydantic
- WebSocket / streaming APIs for real-time chat responses
- API tooling and testing: Bruno / Postman, OpenAPI/Swagger; API gateways
- Async & parallel testing and mocking AWS
- Modern Python tooling: uv, ruff, ty/mypy, pre-commit
- CI/CD with GitHub Actions, and trunk-based development with Conventional Commits
- AWS (ECS/Fargate, Secrets Manager), Terraform, Docker
- Multilingual NLP (Dutch/English)
Create a free Caio profile to unlock more results and save your role and location preferences.
Unlock free search