I build cloud-native AI systems with a focus on healthcare workflows, reliability, and production-grade engineering. I like shipping fast, measuring outcomes, and keeping my work open-source when possible.
- Cloud-native AI/ML systems (deployment, monitoring, secure pipelines)
- Healthcare-adjacent tooling (workflow analytics, evidence builders, productivity systems)
- Developer tooling and automation (fast prototypes → production-ready architecture)
Below are projects that reflect the direction I’m building in: healthcare-grade data + AI pipelines, workflow tooling, and production-ready architecture.
A structured “evidence builder” concept that organizes signals, assumptions, and supporting artifacts into a clean workflow for decision-making and audits.
Repo: https://github.com/yash27-lab/BaselineIQ
A product-style repo focused on turning messy data into usable insight layers (APIs + storage + clean UI patterns).
Repo: https://github.com/yash27-lab/StateScope
A repo for engineering experiments that emphasize correctness, performance, and reproducibility.
Repo: https://github.com/yash27-lab/Provectus
Want this section to be more “wow”? Add 1–2 lines per repo: problem → approach → measurable outcome (latency, cost, accuracy, etc.).
- Big Data for Smart Energy (CRC Press, 2021) — Co-author
- I optimize for real-world constraints: reliability, security, cost, latency
- I like clean architecture: explicit interfaces, measurable pipelines, honest documentation
- I build in public to learn faster and make the work reusable
- LinkedIn: https://www.linkedin.com/in/yash-n-452384142/
- X: https://x.com/realyashnegi
- Instagram: https://instagram.com/realyashnegi
- Email: yashnegi492@gmail.com