Building production AI systems, enterprise backend platforms, and intelligent developer tools.
I build production-grade AI systems that help organizations discover, govern, and interact with enterprise data through intelligent software.
My work combines Applied AI, Backend Engineering, Data Engineering, and Cloud Architecture to ship scalable products used in production.
- 🧠 Enterprise AI Platforms
- 🤖 LLM Applications & RAG Systems
- ⚡ FastAPI Backend Services
- 🔎 Semantic Search
- ☁ Azure Cloud Architecture
- 📊 Databricks & Spark Data Engineering
- 🗂 Metadata & Data Governance
| Metric | Achievement |
|---|---|
| Enterprise Assets Indexed | 76,000+ |
| Search Relevance Improvement | 20–30% |
| Manual Rule-Authoring Effort Reduced | 70–80% |
| API Latency Reduction | 30–40% |
| Metadata Assets Under Lineage Tracking | 10,000+ |
| Production Deployments Sustained | 20+ |
| Enterprise Integrations | 3+ |
| REST APIs Engineered | 20–30 |
| Project | Description | Tech |
|---|---|---|
| 🔍 Rover | Enterprise AI search & knowledge platform for governed metadata retrieval | Azure AI Search · Azure OpenAI · FastAPI |
| ⚡ Augmented Data Quality | AI-assisted enterprise data quality automation and governance | FastAPI · Azure OpenAI · Databricks · Ataccama |
| 📘 Business Glossary Generator | Enterprise metadata automation platform | (details in progress) |
| 🧠 Mnemo | OpenRAG platform for intelligent document search and AI chat | FastAPI · React · RAG · Vector Search |
| 🩺 MedResearch AI | Medical research assistant built for the AWS AI for Bharat Hackathon | React · Docker · RAG · Hybrid Retrieval |
| 🌐 Portfolio | Personal engineering portfolio showcasing AI & backend work | React · TailwindCSS |
GSK (GlaxoSmithKline) · Jan 2024 – Present
Building production AI systems across enterprise metadata management, governance, backend engineering, semantic search, and intelligent automation.
Highlights include:
- Built enterprise semantic search using Azure OpenAI and Azure AI Search, indexing 76,000+ enterprise assets.
- Designed an AI-assisted automation layer that cut manual data-quality rule-authoring effort by 70–80%.
- Developed production FastAPI backend services and 20–30 reusable REST APIs across Databricks, ServiceNow, Collibra, and ARIS.
- Engineered Spark SQL pipelines on Databricks and built a metadata lineage graph with NetworkX.
- Modernized observability with centralized logging and Azure Monitor alerting, cutting API latency by 30–40%.
- Served as Release Lead, coordinating production deployments, release validation, and incident resolution.
At GSK:
- 🥉 2x Bronze Award — Metadata Platform & DevOps Automation; Supply Chain Warriors (Bronze Finalist among 44 teams)
- 🌟 Multiple Quarterly Recognition Awards for enterprise AI innovation, metadata engineering, and AI-powered search
Academic:
- 🥇 Best Project Award — M.S. Ramaiah University
- 🏅 Rank 5 — GeeksforGeeks (University)
- 🏁 Rank 281 — GFG Coding Contest 119
B.Tech — Computer Science & Information Science
M.S. Ramaiah University of Applied Sciences
CGPA: 8.2 / 10
- Microsoft & LinkedIn — Career Essentials in Generative AI
- Google — Responsible AI
- HackerRank — SQL (Advanced)
- Cisco — Introduction to Cybersecurity
- Cognitive Class — Machine Learning with Python
- UiPath × GeeksforGeeks — Build Smarter Scalable AI Agents
- Udemy — Python Masterclass