Agentic AI Engineer

Responsibilities:

• Design and build LLM-powered agentic AI pipelines that reason, plan, and execute multi-step workflows with minimal human intervention.
• Own tool and function calling integrations that connect LLMs to internal APIs, EHR systems, calendars, and third-party services.
• Build and maintain rigorous eval frameworks: offline eval sets, regression suites, latency benchmarks, and safety/guardrail tests.
• Ship and operate LLMs in production: streaming responses, structured outputs, prompt versioning, cost/latency tracking, and observability.
• Architect and maintain distributed microservices that expose LLM APIs in async Python (FastAPI), event-driven pipelines, and real-time WebSocket workflows.
• Build RAG pipelines: chunking strategies, embeddings, vector stores, retrieval tuning, and PHI-safe handling.

Requirements:

• 3+ years of hands-on Gen AI / LLM engineering experience building and shipping real systems, not just research or prototypes.
• 5+ years of hands-on AI / ML engineering experience overall (Gen AI + pre-Gen AI combined).
• Deep LLM expertise in prompt engineering, tool/function calling, structured outputs, chain-of-thought, and agent orchestration (LangChain, LangGraph, or similar).
• Proven eval culture that you treat evals as a first-class concern, not an afterthought.
• Production LLM experience, integrations with OpenAI / Azure OpenAI / AWS Bedrock, streaming, cost optimisation, and monitoring.
• Strong software engineering fundamentals in Python (strong), async programming, REST APIs, CI/CD, Docker.
• Distributed systems fluency with microservice design, event-driven architecture (Kafka/SQS/RabbitMQ), and LLM API gateway patterns.
• End-to-end ownership mindset: you write tests, validate in staging, and don't consider a feature done until it's verified in production.