Das ist der Job
AI / Agents Engineer (Anthropic + OpenAI)
Build production agents on Anthropic + OpenAI direct.
Darum lohnt es sich
RAG, structured extraction, tool-use, evals. ABOUT THE ROLE
Our Agents service line builds custom agents on Anthropic Claude and OpenAI direct — not a chatbot wrapper, not a per-resolution rebrand.
We replace per-resolution agent SaaS (Salesforce Agentforce, Intercom Fin, Zendesk Resolve) with custom agents that run on the client’s own LLM contract. AI Engineers here own the agent architecture, eval harnesses, and production stability.
WHAT YOU’LL DO
Architect agent loops: planner / executor / verifier patterns, tool‑use schemas, retry + escalation logic. Build retrieval pipelines: chunking strategy, vector DB choice (Pinecone / pgvector / Weaviate), reranking, freshness handling. Stay current on Anthropic + OpenAI release notes; adjust prompts + schemas when APIs evolve.
We treat the foundation‑model contract as a dependency, not a vendor. Ship to the client’s AWS + Anthropic / OpenAI accounts. Their tokens, their data, their audit log. MUST‑HAVES
6+ years building production software. At least 1 year shipping LLM‑powered features to real users (not POCs). Strong Python or TypeScript.
Comfortable writing structured‑output schemas (Pydantic, Zod) and reading Anthropic + OpenAI docs as primary references. Has built and maintained an eval harness with regression detection. Knows the difference between vibes‑eval and a real harness. Comfortable cost‑modelling agent loops in tokens.
Can predict spend on a 100K‑resolution / month workload before deploying. NICE TO HAVE
Open‑source contributions to LangChain, LlamaIndex, Anthropic SDK, Instructor, or DSPy. Public writing on agent / RAG architecture. Experience with regulated‑industry deployments (legal, healthcare, financial).
