My journey at Autodesk began with a clear mission: strengthen the backbone of internal engineering workflows. I stepped into an environment where consistency, automation, and reliability were critical — and where subtle improvements in CI/CD pipelines could unlock massive efficiency gains for hundreds of engineers.

I build and maintain highly reliable CI/CD pipelines, automate complex workflows, and design reproducible build environments using Docker and Ansible. My daily work often revolves around profiling failing pipelines, refining dependency graphs, and transforming fragile scripts into predictable, self-healing systems.

From debugging macOS sandbox limitations to rewriting environment bootstrap processes, I’ve focused on reducing friction across build, test, and deployment stages. I work closely with teams to introduce automated verification steps, reduce manual interventions, and ensure highly controlled release cycles — the kind that scale with confidence.

Tech Used: CI/CD · Ansible · Docker · Bash · Python · Node.js · AWS (S3) · Linux/macOS Internals · Automation Architecture

At Justdial, I found myself in the middle of a large-scale transformation — moving from aging monoliths to distributed, event-driven microservices. I led critical migration paths, decomposing tightly coupled PHP systems into modern Node.js services that were leaner, scalable, and easier to evolve.

A major highlight was engineering a high-throughput Excel processing engine that stitched together Node.js, Python, RabbitMQ, and AWS S3. Designed to handle millions of rows without choking, it became a foundational service powering data operations at scale. Along the way, I optimized several APIs, shaving off ~15% response time and improving downstream system performance.

On the frontend, I built a component system of over 100 reusable React components — introducing a level of design consistency and development velocity the team had been missing for years. I also leveraged Python automation for SEO pipelines, which boosted traffic and gave the platform a competitive edge in visibility.

Tech Used: Node.js · React · Python · Redis · RabbitMQ · MongoDB · MySQL · AWS S3 · Distributed Systems · Microservices Architecture

Qnap introduced me to the world of enterprise data protection — backups, restores, encryption, multithreading, and everything that keeps business-critical data safe. I engineered backend services for Boxafe, Qnap’s enterprise backup solution for MS365 and Google Workspace, handling data flow at massive scale across cloud ecosystems.

I redesigned how Teams and SharePoint data was structured across domains and subdomains, resulting in a ~25% performance improvement in data operations. Much of my work involved threading models, I/O optimizations, and ensuring the solution remained efficient when deployed on resource-constrained NAS hardware — where every millisecond and megabyte matters.

Tech Used: Python (Flask) · Multithreading · NAS / On-Prem Systems · Cloud APIs · Data Structuring · Performance Engineering

My early engineering story began at Zebralearn, where I helped build ZebraPro — a research platform designed for analysts, investors, and industry researchers. Here, I discovered the power of Python ecosystems, working with Flask and Django to create high-performance APIs that enabled large-scale PDF processing and advanced document search.

I integrated ML-driven PDF search that transformed static documents into searchable, analyzable sources of insight. I collaborated with the product and research teams to design analytical pipelines that helped shape the platform’s MVP, contributing directly to its early traction and investor interest.

Tech Used: Python · Flask · Django · Machine Learning (NLP Search) · SQL · API Design · PDF Processing