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