Role : AI Lead - Applied AI & Agentic Systems

Lead AI from business vision to production impact - across ML, GenAI and agentic applications.

What you'll own :

- Shape the AI roadmap. Partner with HODs, CXOs and their -1 leaders to identify opportunities, prioritise use cases and translate strategic goals into an executable AI portfolio.

- Architect AI products. Design end-to-end ML, GenAI and agentic solutions across models, RAG, tools, APIs, state, orchestration, human approvals, security and observability.

- Lead agentic delivery. Guide hands-on implementation using LangGraph, CrewAI or similar frameworks; define routing, specialist agents, memory/state, tool permissions, retries and loop controls.

- Drive productionisation. Move priority use cases from discovery to production with evaluation, MLOps/LLMOps, monitoring, fallbacks, cost controls, governance and adoption.

- Lead the team. Set technical direction, review designs and model choices, mentor AI talent and create strong ownership for quality, velocity and measurable outcomes.

- Build across teams. Work with Data, Product, Engineering, Architecture and Business teams to create reusable AI components and remove delivery dependencies.

What you'll bring :

- Hands-on experience. 7 - 10 years in ML/DL or applied AI, including 3+ years implementing GenAI solutions in production.

- Framework depth. Hands-on LangGraph, CrewAI or comparable agent orchestration experience; strong LangChain or similar LLM application framework experience.

- AI/ML depth. Strong ML/DL foundations plus RAG, embeddings, vector search, reranking, model routing, multimodal AI, optimisation and evaluation.

- Strong engineering. Python, APIs/FastAPI, async services, cloud, Git, Docker, CI/CD and practical MLOps/LLMOps; product engineering experience preferred.

- Leadership judgement. Engage CXOs/HODs, simplify complex trade-offs, manage teams and balance business value, architecture, risk, latency and cost.

What will make you stand out :

- Builder + architect. Show systems you helped build end-to-end - from an ambiguous business problem through architecture, implementation, production and adoption.

- Agent depth. Open and debug the graph : state, routing, tools, hand-offs, retries, checkpoints, human approvals, traces and failure paths.

- Ownership under ambiguity. Be self-starting, comfortable with leap goals and able to turn loosely defined leadership intent into measurable execution.

Why this opportunity stands out :

- Enterprise-wide canvas. Build AI across sales, onboarding, underwriting, claims, FWA, service, operations, technology and employee productivity.

- Direct leadership exposure. Work with business leaders to shape where AI should change journeys, decisions and operating models.

- Own the full loop. Lead from problem discovery and roadmap through architecture, delivery, production adoption and impact measurement.

- Shape the AI platform. Define reusable agents, RAG patterns, evaluation standards, engineering guardrails and ways of working for Enterprise AI.

What your first six months can look like :

- Learn and prioritise. Understand journeys, data, systems and the current AI estate; align leadership on the highest-value portfolio and success metrics.

- Ship meaningful outcomes. Take priority ML/GenAI/agentic use cases into controlled production with measurable business impact and strong operating controls.

- Raise the bar. Establish architecture and review standards, coach the team and package successful patterns for reuse across functions.

Your impact at Star Health :

- Build for : sales effectiveness; onboarding and underwriting; claims and FWA; customer experience; employee productivity; AI for technology and operations.

- Success looks like : a team repeatedly ships safe, scalable AI products that are adopted, measurable and improve turnaround time, quality, cost, conversion or customer effort.

READY TO BUILD? Bring a story where you turned a senior leader's ambition into an AI roadmap and production outcome.