Core skills (must-have)

- Strong Python FastAPI — async endpoints, background tasks, streaming responses, pydantic models. This is the backbone of every app.
- LLM/agent engineering — practical experience with agent frameworks (pydantic-ai, LangGraph/LangChain, or similar), prompt design, tool-calling, and RAG. Critically: understanding failure modes — hallucination, context contamination, non-deterministic output. Much of the recent work has been debugging exactly these.
- RAG in depth — chunking, embeddings, vector search (Qdrant/pgvector/etc.), retrieval quality tuning, and citation/grounding. Knowing why retrieval returns the wrong thing.
- React (hooks) solid CSS — building and, importantly, fixing real UI: layout, responsive behavior, streaming state. Not just wiring components — being able to look at a screen and know why it's broken.




- Concurrency & state discipline — a recurring class of bugs here has been shared mutable state / callback bleed under concurrent requests. Someone who instinctively reaches for request-scoped state, locks, and thread-safety.
- Docker Kubernetes fluency — comfortable with containers, deployments, debugging pods/logs. Doesn't need to be a platform expert but must operate the dev loop.
- Testing rigor — writes pytest vitest, uses mocks/fixtures, tests before shipping. This codebase has real test coverage and expects it maintained.
- Debugging & reading code — the single most valuable trait. Able to trace a symptom ("answers the wrong question") to a root cause across frontend, backend, and agent layers.

📌 Generative AI Engineer (Barcelona)
🏢 PRIMUS Global Solutions (PRIMUS UK u0026amp; Europe)
📍 Barcelona