Core skills (must-have)

1. Strong Python + FastAPI

— async endpoints, background tasks, streaming responses, pydantic models. This is the backbone of every app.

1. 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.

1. RAG in depth

— chunking, embeddings, vector search (Qdrant/pgvector/etc.), retrieval quality tuning, and citation/grounding. Knowing why retrieval returns the wrong thing.

1. 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.

1. 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.

1. Docker + Kubernetes fluency

— comfortable with containers, deployments, debugging pods/logs. Doesn't need to be a platform expert but must operate the dev loop.

1. Testing rigor

— writes pytest + vitest, uses mocks/fixtures, tests before shipping. This codebase has real test coverage and expects it maintained.

1. 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 u0026 Europe)
📍 Barcelona