Software Engineer at Microsoft. I build the data infrastructure that makes AI systems reliable for the people who depend on them.
Currently:
What I Believe
Everything I build serves someone
Infrastructure doesn't exist in a vacuum. Data quality frameworks matter because real users get wrong answers when signals are bad. Pipeline reliability matters because teams downstream are blocked when systems break. I start with who's affected and work backwards to the engineering.
Fundamentals over frameworks
System design, data modeling, algorithmic thinking, and engineering rigor transfer across any stack. I'd rather work with someone who deeply understands distributed consensus than someone who memorized an API.
LLMs are only as good as their data
Everyone is racing to build better models. Fewer people are obsessing over the quality of what goes in. The biggest gains in AI right now come from data validation, signal processing, and evaluation infrastructure.
Boring infrastructure wins
The best data platforms are invisible to the people who depend on them. They process billions of events, never lose data, and nobody has to think about them. That invisibility is the product.
Get in Touch
Open to conversations about engineering roles, data infrastructure, and AI systems at scale.