Projects that moved
AI forward
10 case studies across AI labs, enterprises, and platform teams, each built on reproducibility, rigour, and real engineering depth.
MLOps Reproducible Training Pipeline
Built a fully reproducible training pipeline for a health-tech company using Dockerized environments and DVC-tracked datasets. Every experiment links its dataset version, model config, and hardware spec, enabling exact replay of any run.
↳ Reproducibility, audit-ability, and regulatory compliance achieved.
Enterprise CI/CD & DevSecOps Overhaul
Redesigned the CI/CD pipeline for a large e-commerce platform using GitHub Actions, adding automated testing, security scanning, and zero-downtime deployment workflows that accelerated release velocity by 3×.
↳ 3× faster releases, integrated security, improved developer confidence.
Repository Refactoring & Test Coverage Uplift
Comprehensive audit of an open-source Java project, refactored brittle modules, wrote unit and integration tests, and stabilized flaky tests. Coverage went from 34% to 87%.
↳ Code quality, maintainability, and 87% test coverage.
Remote Engineering Pod for Ed-Tech Platform
Deployed a senior engineering pod to build a collaborative learning platform. Delivered key features, mentored junior staff, and maintained 98% sprint completion rate across overlapping time zones.
↳ Continuous delivery, knowledge transfer, and flexible scale.
Rubric-Based LLM Scoring Suite for FinTech
Built a domain-specific evaluation suite for a financial-services firm, benchmarking LLMs on factual accuracy, regulatory compliance, and instruction-following using gold-standard datasets and containerized pipelines.
↳ Transparent, reproducible evaluation with fair model comparisons.
Privacy-Focused LLM Evaluation for Healthcare
Developed a HIPAA-aligned evaluation harness for medical language models with de-identified datasets and rubric-based criteria that respect patient privacy while enabling rigorous model comparison.
↳ Compliant, reproducible evaluation with secure audit trails.