Technical Product Manager - Gen AI

Job Title: Technical Product Manager - Gen AI Posting Start Date: 18/08/2026 Job Description Job Title: Technical Product Manager, Gen AIWe are a technology-led healthcare solutions provider. We are driven by our purpose to enable healthcare organizations to be future-ready. We offer accelerated, global growth opportunities for talent that's bold, industrious, and nimble. With Indegene, you gain a unique career experience that celebrates entrepreneurship and is guided by passion, innovation, collaboration, and empathy.Looking to jump-start your career? We understand how important the first few years of your career are, which create the foundation of your entire professional journey. At Indegene, we promise you a differentiated career experience. You will not only work at the exciting intersection of healthcare and technology but also will be mentored by some of the most brilliant minds in the industry. We are offering a global fast-track career where you can grow along with Indegene's high-speed growth.We are purpose-driven. We enable healthcare organizations to be future ready and our customer obsession is our driving force. We ensure that our customers achieve what they truly want. We are bold in our actions, nimble in our decision-making, and industrious in the way we work.We are looking for a Technical Product Manager with hands-on experience building Gen AI prototypes — validating ideas through rapid experimentation and turning findings into clear decisions that determine what graduates into a full solution. Must Have Build prototypes and proofs-of-concept, fast — take a problem statement and produce a working agentic demo that proves (or disproves) the approach and gives stakeholders something concrete to react to. Experiment with agentic patterns — prototype single- and multi-agent systems that plan, use tools, call APIs, and complete multi-step tasks; try different orchestration approaches and learn which fit the problem. Explore agent interoperability — prototype with MCP (Model Context Protocol) to connect models to tools, data, and context, and experiment with A2A (Agent-to-Agent) patterns for agents that coordinate and delegate. Refine model behavior for the use case — get to a good-enough result through prompt engineering, retrieval-augmented generation (RAG), and lightweight tuning; benchmark models against the task to pick the right one. Stand up quick RAG pipelines — embeddings, vector search, and grounding sufficient to demonstrate accuracy and traceability in a prototype. Prove out ideas with lightweight evaluation — define what “good” looks like for each experiment and measure against it, so decisions to pursue or park an idea are evidence-based. Build simple app experiences around prototypes — enough of a UI or API for stakeholders to interact with the concept directly. Package learnings for the next phase — document what worked, where the risks are, and what it would take to productionize, so promising prototypes can graduate into full solutions. Drive build–show–learn loops with product, engineering, and domain teams. What you'll bring (required) 3+ years in a technical product management or software or ML engineering role, with hands‑on experience building LLM / Gen AI prototypes or applications. Strong Python and the ability to move quickly with unfamiliar tools and APIs. Hands‑on experience prototyping with agentic frameworks such as LangGraph, LangChain, LlamaIndex, CrewAI, AutoGen, or the OpenAI/Anthropic Agents SDKs — tool use, function calling, and basic orchestration. Familiarity with MCP and/or A2A, or a clear ability to pick them up quickly and wire agents to tools and to each other. Practical RAG experience and familiarity with a vector store (e.g., pgvector, FAISS, Pinecone, Weaviate). Strong prompt engineering skills and comfort working across foundation models from multiple providers (e.g., Anthropic Claude, OpenAI, open‑weight models). A bias for action and rapid iteration — you’d rather ship a rough working demo and learn than over‑engineer in the abstract. Sound judgment on when a prototype is “good enough” to make a decision. Good to HaveNice to have: Experience taking prototypes toward production — deployment, LLMOps/MLOps tooling (LangSmith, LangFuse, MLflow), and observability. (This is where the role can grow.) Fine‑tuning / PEFT (LoRA/QLoRA) or work with smaller task‑specific models. Building MCP servers for real systems or contributing to open‑source agent/MCP tooling. Multi‑agent orchestration and asynchronous agent workflows. Frontend skills (React/TypeScript) for richer prototype experiences. Exposure to regulated or enterprise environments; life‑sciences / healthcare domain knowledge is a plus. Cloud experience (AWS / Azure / GCP).

EQUAL OPPORTUNITY

Indegene is proud to be an Equal Employment Employer and is committed to the culture of Inclusion and Diversity. We do not discriminate on the basis of race, religion, sex, colour, age, national origin, pregnancy, sexual orientation, physical ability, or any other characteristics. All employment decisions, from hiring to separation, will be based on business requirements, the candidate's merit and qualification. We are an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, colour, religion, sex, national origin, gender identity, sexual orientation, disability status, protected veteran status, or any other characteristics. #J-18808-Ljbffr Indegene Inc.