ML/AI engineer contributing to CNCF and Red Hat projects because I believe credibility is built through shipped code, not certificates. Targeting GSoC and LFX. Not the fastest, but consistent.

• Backend Intern at TheCodeV Ltd — built FastAPI systems serving 1000+ requests
• Published CheckMate AI on PyPI — AI security scanner with 22% precision improvement
• Built jmcp — a Go CLI for Jaeger trace debugging while actively contributing to Jaeger upstream
• ML/AI Co-Lead at GDGoC LGU

jmcp — incident debugging CLI for Jaeger via MCP. From "something is broken" to documented root cause without leaving the terminal. Written in Go. Single static binary. Zero external dependencies. 3 releases shipped.

jmcp search --errors --since 15m frontend   # find failing traces
jmcp blame @1                               # root cause in one command
jmcp report --title "Checkout 500s" @1     # incident report, done

The thing about this project: I built it while contributing PRs to Jaeger upstream. Using the tool to understand the codebase. Then improving the codebase with what I learned. That loop is how I actually get things done.

Updated each contribution cycle. Full breakdown in Open_Source_Contributions.

Projects I've touched: Jaeger, krkn, Meshery, Vitess, KubeEdge/Ianvs, Kubeflow, Drasi, sktime, PyCaret.

Languages

ML / AI

Backend & Cloud