Healthcare AI
Executeml Healthcare AI
Executeml needed production AI features for healthcare workflows where reliability, privacy, and audit-friendly architecture mattered more than demos.
Application layer for authenticated product workflows.
Backend services for ingestion, retrieval, and AI orchestration.
Deployment pipeline for repeatable releases and operational feedback.
Full Stack Product & AI Engineer
2024-2026
Private case study
FastAPIPythonNext.jsRAGVector searchCI/CD
Problem
Healthcare teams needed a way to work with unstructured medical information through AI-assisted flows without turning sensitive data handling into an afterthought.
Outcome
Shipped healthcare AI product infrastructure that made RAG workflows usable inside production-facing product constraints.
Constraints
- Design backend flows around private healthcare data and least-exposure principles.
- Keep retrieval behavior explainable enough for product and engineering review.
- Support deployment workflows that could be maintained by a small product team.
Contribution
- Built backend and product surfaces for healthcare AI workflows.
- Worked on retrieval architecture for unstructured medical data.
- Supported CI/CD and production delivery practices around the platform.
Architecture
- Application layer for authenticated product workflows.
- Backend services for ingestion, retrieval, and AI orchestration.
- Deployment pipeline for repeatable releases and operational feedback.
Sanitized case study: implementation details are intentionally generalized to avoid exposing private healthcare data or internal systems.