 
						Ai Engineer
3 weeks ago
Role - LLM Workflow Builder – design and wire up prompts, tools, memory and agents with LangChain / LangGraph / LangServe. - API & Micro-Service Owner – ship FastAPI endpoints (REST + streaming) that are secure, observable and lightning fast. - End-to-End Engineer – own the full lifecycle: code → container → CI/CD → K8s → monitoring, with a bias for rapid, iterative releases. - Cross-Functional Partner – collaborate with product and design to scope MVPs, estimate effort, and keep delivery on track. Responsibilities - Architect, implement and maintain multi-agent workflows and retrieval-augmented pipelines. - Write clean, typed, tested Python (async, pytest, poetry). - Instrument and evaluate everything with LangSmith / LangFuse; build guardrails and regression suites for prompts. - Containerize (Docker) and deploy (Kubernetes, GitHub Actions) with automated rollbacks and alerts. - Optimize latency, throughput and cost; troubleshoot productionissues end-to-end. - Mentor teammates on best practices in LLM app engineering and DevOps. Must-Have Skills - Strong Python craftsmanship and async I/O expertise. - Proven experience with LangChain or LangGraph in real projects. - FastAPI (or similar) for building REST / streaming APIs. - Solid DevOps: Docker, basic Kubernetes, CI/CD pipelines, observability tooling. - Git proficiency: PR hygiene, code reviews, and clear documentation. Nice-to-Haves - Lightweight fine-tuning (LoRA/QLoRA) or embedding-model selection. - Vector databases (Postgres/pgvector, Milvus, LanceDB). - Serverless / edge deploys (Fly.Io, Cloudflare Workers, AWS Lambda). - Experience with multi-agent planning frameworks or autonomous tool-use research.
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