Das ist der Job
Build the evidence-first retrieval and reasoning core powering RAIVA across regulated industries.
Darum lohnt es sich
About the role You will own large parts of our multi-agent retrieval, evidence verification, and consortium scoring stack. We care about provable answers — not vibes — so your work directly determines whether a legal, financial, or real‑estate professional can trust an answer enough to act on it.
What you'll do Design and ship multi-agent retrieval pipelines (SQL, BM25, vector, optional graph) Own embedding strategy across primary (Cohere v4) and domain‑specific models Improve evidence extraction, verification, and consortium scoring quality Define evals and guardrails so quality regressions are caught before users see them Collaborate with backend/MLOps to take experiments to stable, observable production What we look for 5+ years building production AI/ML or applied LLM systems Deep practical experience with RAG, hybrid search, embeddings, and rerankers Strong Python; comfortable with FastAPI, async workloads, and vector DBs (Qdrant) Track record of shipping evidence-backed or high‑stakes AI features Nice to have Experience with Cohere, OpenAI, Gemini, or domain‑tuned embedding models Prior work in legal, financial, tax, or real‑estate tech Open‑source contributions in retrieval/LLM tooling Stack & tools Python FastAPI Supabase Qdrant LangChain Cohere OpenTelemetry Docker #J-18808-Ljbffr
