AI Engineer

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 production issues 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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