Some are polished. Some are still duct-taped. All are mine.


🛠️ End-to-End Logistic Regression MLOps Pipeline

Role: ML Engineer (Personal Project)
Duration: Started July 2025 — still ongoing
URL: GitHub – mlops

Built from scratch (yes, with pain and glory) — a full MLOps regression pipeline focused on the “real-world mess”:

  • CI/CD automation
  • Experiment tracking with MLflow
  • Data & model versioning via DVC
  • Drift detection
  • Cloud-native deployment with Kubernetes

Currently adding more chaos (Airflow, monitoring, maybe a robot arm). Trying to simulate production without actually breaking production.


📚 Curriculum LLM Platform

Role: Core Developer (Collaborated with Project Owner)
Duration: Feb 2025 – Jul 2025

Also built from scratch — a lightweight content delivery platform with:

  • Payload CMS (Node.js), Next.js frontend
  • Python FastAPI backend
  • Multi-env Docker setup (local/dev/staging) with GitHub Actions
  • Modular RAG pipeline (because static content is boring)

Designed for edge inference on Intel XPU devices — think “LLM-on-the-go” kind of vibe.

⚠️ Internal tool, so no public repo. But yes, it does work and didn’t explode.


🧠 Enterprise GenAI Platform (Intel OPEA)

Role: Core Developer (under Tech Lead Direction)
Duration: Nov 2024 – Jan 2025
URL: GitHub – GenAIExamples

Optimized containerized GenAI inferencing on Intel iGPU/dGPU (XPU):

  • Debugged docker container hell
  • Migrated workflows from systemd to Docker Compose
  • Reduced setup friction across teams
  • Built internal PoC demos to show off inferencing

🔍 AI Workload Validation (Intel WSF)

Role: Validation Support Engineer
Duration: Jul 2024 – Oct 2025
URL: GitHub – Workload Services Framework

Worked on validation support for AI workloads on Intel Xeon platforms:

  • Wrote and ran test cases
  • Helped triage bugs using JIRA (my best frenemy)
  • Helped stabilize and cover more edge cases in platform testing

Learned that testing is 30% logic, 70% caffeine.


🎨 Final Year Project – Colorizing Grayscale Images with Deep Learning

Role: Researcher
Duration: Oct 2022 – Jul 2023
More info: LinkedIn Project

Built a conditional GAN model (“Colour Stack cGAN”) to colorize grayscale images:

  • Literature review + model architecture from scratch
  • Trained using paired datasets (gray ↔ color)
  • Evaluated perceptual quality and accuracy
  • Use cases: restoring old photos, automatic media colorization, digital nostalgia

Presented at academic symposiums, and no one fell asleep — a win.


This page will self-update whenever I break/ship something cool.