ArtiD

About the role

We're building an on-premise AI platform that processes large volumes of multilingual documents and runs autonomous agents that gather and analyse information from open sources. Everything runs on our own hardware: self-hosted models, no external APIs.

You'll own the agentic layer: the LangGraph applications that plan, retrieve and reason, the prompt engineering behind them, and the evaluation that keeps their quality measurable.

What you'll work on

• Multi-step agents on LangGraph: planning loops, tool use, checkpointing, crash recovery, human-in-the-loop
• Prompt engineering with schema-constrained decoding, where every response is validated against a contract
• RAG pipelines: chunking, hybrid dense + sparse retrieval, citation-grounded generation
• Information extraction from documents into structured records
• Evaluation: gold sets, regression gates, scenario tests over full agent trajectories
• Prompt-injection defence: policy layers over tool calls, adversarial test suites

Stack

Python, LangGraph, FastAPI, vLLM, Qdrant, OpenSearch, Postgres, Kubernetes, MLflow, inspect_ai

What we're looking for

• 4+ years in Python, with at least 1 year building LLM-based systems in production
• Hands-on experience with an agent framework (LangGraph, Pydantic AI, or similar), something you've shipped rather than prototyped
• Solid RAG experience: you understand why retrieval quality, not model choice, is usually the bottleneck
• Practical prompt engineering: structured outputs, evaluation-driven iteration
• Experience with self-hosted inference (vLLM, TGI, Ollama)
• Strong engineering discipline: typed code, tests, reproducibility

Nice to have

Document processing at scale (OCR, layout extraction) · information extraction or knowledge graphs · LLM evaluation tooling · air-gapped environments · web crawling with robots compliance and rate limiting