TapHealth
AI Engineer
Built production clinical-AI systems for diabetes care, including expanded CDSS coverage, lab-aware context pipelines, Tara coaching workflows, and pre-release evaluation and observability loops.
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
- Expanded the CDSS from Type 1 and Type 2 diabetes plus a few adjacent comorbidities to 120 modeled conditions: 87 chronic and 33 acute.
- Built a lab-context layer covering 45 normalized lab-test parameters across 362 graph nodes and naming variants to support care-plan updates and follow-ups informed by lab results.
- Built Tara as a contextual AI health coach across WhatsApp and in-app surfaces; the sourced role record attributes rollout-period growth of daily active users +164%, sessions +187%, and messages +117%.
- Built an in-house pre-release evaluation harness with fixtures, personas, rubrics, LLM-as-judge checks, and live validation; Langfuse was used for post-release observability and pre-release prompt experiments.