About Truveta
Truveta transforms healthcare into intelligence. Built with and owned by US health systems united by a mission of Saving Lives with Data, Truveta powers breakthrough discoveries, auditable data fit for regulatory submissions, and AI that improves patient care.
At Truveta, we are building something that has never existed before, bringing together healthcare, science, and technology to tackle some of the most important challenges in human health. Our mission calls us to think big, move with urgency, and build with purpose. We are looking for exceptional people who are energized by hard problems, inspired by the opportunity to improve health, and committed to building a company and culture where talented people can do their best work.
Our team is a special combination of software, health, and big data experience. We are building a talented team of passionate, mission-oriented, and innovative thinkers.
Who We Need
Join Truveta's Intelligence organization and lead a team of engineers building the next wave of healthcare AI. Your team will build adaptive agentic systems that keep learning, reason over complex data and real customer use cases, and drive real-world impact in healthcare. Guided by Truveta's mission of Saving Lives with Data, you'll help turn health data into intelligence that clinicians and researchers can use to make proactive, data-driven decisions.
We're looking for an Intelligence Pod Lead at the Truveta India Development Center (IDC): a hands-on technical leader with depth in applied agentic AI and large-scale engineering, a solid grounding in machine learning fundamentals, and a record of helping the engineers around them do their best work. You'll set your pod's technical direction, own what it delivers from start to finish, and still write code and make the key architecture calls. You'll grow a high-performing team that ships scalable, self-improving AI agents and the platform systems that support them. These systems will learn from large and varied healthcare data and deliver useful insights through automation and reasoning.
Location: Hyderabad, India.
This Opportunity
Patients, clinicians, medical researchers and healthcare decision makers deserve the same large-scale innovation and AI that has already transformed how people work, create and communicate in many other fields. As an Intelligence Pod Lead, you'll turn ambitious AI ideas into reliable production systems that improve research and development, business strategy, operations and care delivery across healthcare.
You'll own a focused problem space end to end: defining the technical roadmap, breaking down ambiguous problems, guiding design and evaluation, and making sure your team delivers measurable results. You'll be a force multiplier. Your judgment, mentoring and technical leadership will raise the bar for your pod and for the wider Intelligence organization.
If you want purposeful work and the chance to influence how AI supports learning, decision-making and discovery in healthcare, this role lets you lead and keep growing. You'll work in a fast-moving, collaborative environment where your leadership directly shapes how healthcare becomes more connected, intelligent and impactful.
What You'll Do
- Lead the team's technical direction. Define and own the technical vision, architecture and engineering roadmap for your pod's area, such as agentic workflows, knowledge curation and retrieval, structured reasoning and automated evaluation. Make pragmatic trade-offs between innovation, performance, cost and reliability.
- Architect agentic AI systems. Design multi-agent workflows with frameworks such as LangChain/LangGraph, AutoGen or CrewAI that combine reasoning, planning, memory and tool use. Apply strong context engineering, agent harnesses and deterministic guardrails so long-running agents stay reliable. Use interoperability protocols such as MCP, A2A and Agent Skills to build goal-driven systems that work smoothly with other agents and services, and use observability and evaluation tools such as LangSmith to drive continuous improvement.
- Drive knowledge curation and retrieval strategy. Oversee the design of retrieval-augmented generation (RAG) pipelines, knowledge graphs built from unstructured documents, embedding strategies and vector stores (e.g., FAISS, Pinecone, Chroma, Azure AI Search) that ground reasoning in trusted healthcare data.
- Build and develop the team. Hire, mentor and coach engineers, set a high bar for engineering quality, and build a culture of ownership and fast, thoughtful iteration. Partner closely with product, data and engineering counterparts across time zones.
- Champion responsible AI. Make sure your team's systems reflect ethical AI principles and meet privacy, security and regulatory expectations for healthcare data, so healthcare intelligence advances responsibly at scale. Build in agent security (defenses against prompt injection and tool poisoning, sandboxing, and agent identity with least-privilege access) and human-in-the-loop safeguards against automation bias in clinical settings.
- Keep learning and help the team learn. Stay ahead of new AI architectures, agentic frameworks and approaches to reasoning and retrieval. Bring curiosity, humility and a bias toward iteration, and help the team adopt new ideas thoughtfully.
Required Qualifications
- 8+ years of experience building and deploying scalable, production-ready ML systems in a collaborative engineering environment.
- 2+ years in a technical lead or similar role, including setting technical direction and mentoring engineers.
- A track record of taking complex ML or AI systems from concept to production with measurable impact.
- Deep, hands-on proficiency with agentic AI frameworks (e.g., LangChain/LangGraph, AutoGen, CrewAI) and a working understanding of interoperability protocols such as MCP, A2A and Agent Skills.
- Experience building production agent harnesses with guardrails and context engineering.
- Experience designing and operating semantic search systems with knowledge graphs and/or RAG, vector databases and embeddings (FAISS, Pinecone, Chroma, Azure AI Search or similar).
- Strong expertise in agentic evaluation, including designing metrics, evaluators or reward models for continuous improvement, and evaluating agents at the agent and trajectory level.
- Strong software engineering fundamentals, with expert proficiency in Python and experience designing scalable systems in modern cloud environments (Azure, AWS or GCP).
- Excellent communication skills, including explaining technical trade-offs clearly to engineers, product partners and leadership, and working effectively across time zones and geographies.
- B.S. or M.S. in Computer Science, Artificial Intelligence or a related technical field.
Preferred Qualifications
- Experience with healthcare, life sciences or other regulated data (e.g., EHR, claims, clinical text), including privacy and compliance considerations.
- Experience running ML experiments and training at scale, including distributed training, GPU optimization and inference serving.
- Experience with MLOps and LLMOps practices: CI/CD for models, observability, drift monitoring and production evaluation loops.
- Publications, open-source contributions or patents in applied ML, NLP or agentic systems.
.png?1742563293){kind=logo}