Agentic AI Engineer - Healthcare EcoSystem

Key Responsibilities : - Design and build LLM-powered agentic AI pipelines that reason, plan, and execute multi-step workflows with minimal human intervention. - Own tool & function calling integrations 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 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. - Take full ownership and conduct end-to-end testing for everything you build from unit and integration tests through to production validation and post-deployment monitoring. - Collaborate closely with product, backend, and QA to define AI requirements and ship reliable, safe features. Qualifications : - 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. - Preferred SkillsNot required for hiring but these make you stand out: - LLM post-training experience with fine-tuning, RLHF, DPO, or LoRA for domain-adapted or instruction-tuned models. - Automatic prompt optimisation familiarity with tools or techniques like DSPy, TextGrad, or automated prompt search for systematic prompt improvement. - Healthcare industry experience, working knowledge of EHR systems, HL7/FHIR standards, HIPAA compliance, or clinical workflows. - Startup experience where you've worked in a fast-moving, resource constrained environment where you wore multiple hats and shipped quickly. - Scaling & on-prem deployments experience deploying LLMs at scale, including self-hosted / on-prem model serving (vLLM, TGI, Triton) or hybrid cloud architectures. Requirements added by the job poster : - 5+ years of work experience with Applied Machine Learning - 3+ years of work experience with Generative AI - 3+ years of work experience with Python (Programming Language