About
I use ML to solve real business problems — not just to build interesting models. At Microsoft, I work on the health and quality of Azure, helping the platform stay reliable for its customers at global scale.
What drives me is understanding a business’s most important challenges, then figuring out how ML can help — quickly, cleanly, and with impact. I work iteratively: scoping tightly, shipping early, and improving based on feedback from the system, the data, and the people using it.
Translate problem
I break down complex problems into smaller, solvable chunks.
Scalable and Secure by Design
A good system is designed deliberately to be scalable and secure. I build systems that are easy to reason about, debug, and extend.
Keep it simple
I believe complexity is an outcome of interaction between simpler components.
Selected Projects
Distributed Model Training Platform
Built a fault-tolerant distributed training system that reduced model training time by 60% while handling dynamic resource allocation across thousands of GPUs.
Real-time Recommendation Engine
Designed and deployed a low-latency recommendation system serving 10M+ requests per day with sub-100ms response times.
AutoML Pipeline Framework
Created an end-to-end AutoML platform that democratized ML model development, enabling non-experts to build production-ready models.
Recent Writing
Let's Connect
Always interested in discussing ML systems, sharing ideas, or exploring opportunities to build something meaningful together.