M. Nobinur
I build agentic AI and retrieval systems — and the evaluation harnesses that tell you whether they actually work.
Skills
Full profile →What I reach for daily, what I'm comfortable in, and what I'm still learning — each one labelled, so you can tell the difference without asking.
Programming
- Python, SQLPrimary tool
- JavaScript / TypeScript, C++Comfortable
- R, MATLABFamiliar
AI & Agents
- RAG, LangGraph, LangChainPrimary tool
- CrewAI, Vercel AI SDK, Mem0Comfortable
ML & Data
- pandas, NumPy, Evaluation & benchmarkingPrimary tool
- PyTorchComfortable
Backend
- FastAPIPrimary tool
- Django, Next.jsComfortable
Data & Cloud
- PostgreSQLPrimary tool
- MySQL, Google Cloud PlatformComfortable
- DuckDBFamiliar
Tooling
- Git, Linux / UnixPrimary tool
- DockerComfortable
Experience
Two years at Jade & Sterling, across a promotion from software engineering into the AI workflow layer — which I now own end to end.
Jade & Sterling
AI Engineer
Own the AI workflow layer end to end — retrieval and agent orchestration through to the evaluation harnesses that gate a release.
- Design and ship production RAG and multi-agent systems with LangChain, LangGraph, CrewAI, and Mem0 — combining retrieval, tool use, orchestration, and persistent memory into reusable services rather than one-off scripts.
- Built the evaluation layer these systems are judged on: retrieval-quality scoring, response-consistency checks, and deterministic regression suites that catch silent degradation before it reaches users.
- Expose workflows as versioned FastAPI services with typed request/response contracts and explicit failure handling, so downstream teams integrate against a stable interface instead of a moving target.
- Containerise and deploy with Docker on Google Cloud Platform, giving every experiment a reproducible path from notebook to production.
Jr. Software Engineer
Backend and full-stack delivery across internal services and client-facing product features.
- Shipped backend services and web features across Python, Django, FastAPI, JavaScript, Next.js, PostgreSQL, and MySQL.
- Integrated third-party APIs and modelled the database-backed flows behind them, replacing manual reconciliation between internal services and user-facing features with automated data exchange.
- Debugged and containerised services in Linux environments, and used Git-based review to keep team delivery predictable.
Selected Work
All work →Visual Saliency Evaluation for ASD and Typically Developing Groups
- PyTorch
- MATLAB
- Google Colab
- NumPy
MutOracle-RAG
- Python
- PyTorch
- DuckDB
- RAG Evaluation
Version-Aware Lazy Segment Tree
- C++17
- CMake
- GoogleTest
- Sanitizers
sIndeX
- Python
- Google APIs
- SEO Automation
Education
Credentials →M.S. in Computer Science and Engineering
Graduate coursework chosen around software correctness and experiment design — the two disciplines production AI work keeps asking for.
B.Sc. (Honours) in Statistics
Four years of estimation, inference, and the ways data misleads you — the foundation I now apply to reading machine-learning benchmarks for what they actually measure.
Higher Secondary School Certificate (HSC), Science
Science group — the mathematics and physics groundwork that later made both statistics and algorithms approachable rather than intimidating.
Writing
All writing →When Your Metrics Disagree: Evaluating Saliency Models on ASD and TD Fixation Data
- Research
- Computer Vision
- Evaluation
Shipping RAG That Survives Contact With Real Users
- RAG
- Engineering
- Evaluation
What Building sIndeX Taught Me About Handing Over Code
- Building
- Career