TapHealth
AI Engineer
Built agentic clinical-AI systems for diabetes care, spanning a contextual health coach, graph-backed decision support, lab-report interpretation, and pre-release evaluation.
Follows the TapHealth AI Engineer Intern entry immediately below and is represented as a sequential role change without claiming a formal promotion.
Evidence-backed highlights
- Built Tara, a contextual AI health coach across WhatsApp and in-app surfaces with Vercel AI SDK, MCP tools, and Hindsight; during rollout, daily active users grew 164%, sessions 187%, and messages 117%.
- Built a graph-backed clinical decision support system modeling 120 conditions—87 chronic and 33 acute—to ground personalized diabetes care plans and proactive check-ins with contextual retrieval.
- Built a lab-report interpretation pipeline covering 45 normalized lab-test parameters across 362 graph nodes and naming variants, linking them to doctor recommendations and lab-aware Tara follow-ups.
- Built an in-house pre-release evaluation harness with fixtures, personas, rubrics, LLM-as-judge checks, and live validation; Langfuse covers post-release observability and prompt experiments.