•      Took the GenAI personalisation programme from prototype to production — a RAG + LLM engine (GPT-4 / Claude) now running live for 5M+ members, directly linked to measurable improvements in health engagement and member retention across 12 markets.

•      Designed and shipped three autonomous Agentic AI systems from scratch: a health-nudge agent that decides timing and content per member, an anomaly detection agent watching data pipelines in real time, and a self-healing agent that identifies and fixes pipeline failures without human intervention — together cutting manual ops overhead by 55%.

•      Built the LLMOps and MLOps foundations the organisation did not have — introduced MLflow, LangChain, and MCP, defined context-engineering standards, and created the deployment and monitoring practices that make AI reliable in production rather than just impressive in a demo.

•      Govern a $50MN+ technical portfolio; sustained 99.5%+ uptime across cloud-native microservices and data infrastructure on AWS while the platform scaled through rapid product growth.

•      Leading a 0-to-1 data platform consolidation — retiring 3 legacy systems and rebuilding as a unified governed data mesh, giving the business a single source of truth for analytics, reporting, and ML features for the first time.

•      People & org: manage 4 senior technical leads across India and the US; personally accountable for hiring bar, performance calibration, and career development for a 70+ person organisation. Took deliberate steps to build a culture where engineers move toward problems rather than away from them.

•      Took the GenAI personalisation programme from prototype to production — a RAG + LLM engine (GPT-4 / Claude) now running live for 5M+ members, directly linked to measurable improvements in health engagement and member retention across 12 markets.

•      Designed and shipped three autonomous Agentic AI systems from scratch: a health-nudge agent that decides timing and content per member, an anomaly detection agent watching data pipelines in real time, and a self-healing agent that identifies and fixes pipeline failures without human intervention — together cutting manual ops overhead by 55%.

•      Built the LLMOps and MLOps foundations the organisation did not have — introduced MLflow, LangChain, and MCP, defined context-engineering standards, and created the deployment and monitoring practices that make AI reliable in production rather than just impressive in a demo.

•      Govern a $50MN+ technical portfolio; sustained 99.5%+ uptime across cloud-native microservices and data infrastructure on AWS while the platform scaled through rapid product growth.

•      Leading a 0-to-1 data platform consolidation — retiring 3 legacy systems and rebuilding as a unified governed data mesh, giving the business a single source of truth for analytics, reporting, and ML features for the first time.

•      People & org: manage 4 senior technical leads across India and the US; personally accountable for hiring bar, performance calibration, and career development for a 70+ person organisation. Took deliberate steps to build a culture where engineers move toward problems rather than away from them.