Senior Software Engineer • AI Engineer • Inference Systems
7+ years building production systems at scale (JPMorgan Chase) | Now focused on AI engineering, LLM systems & optimizing inference at scale
JPMorgan Chase
Senior Software Engineer
~7 years building at scale
Distributed systems & platform
AI Engineering
LLM systems & RAG pipelines
Inference optimization at scale
Applied ML in production
Open to roles
Senior SWE / AI Engineer
Building startup projects
Fast, high-quality shipping
I'm an engineer who's shipped mission-critical software at one of the world's largest financial institutions—and I like owning problems end-to-end. Lately I'm channeling that same rigor into AI: building LLM-powered systems that are not just impressive in a demo, but fast, efficient, and reliable in production—squeezing latency and cost out of inference at scale.
Designed and shipped production systems serving enterprise-scale traffic in a highly regulated environment. Owned services end-to-end with strong reliability, security, and compliance requirements.
Building retrieval-augmented and agentic LLM applications—owning the full pipeline from retrieval and prompting to evaluation and guardrails. Focused on making AI output testable and observable.
Making LLM inference faster and cheaper: batching, caching, quantization, and serving optimizations to cut latency and cost. Bringing enterprise performance-engineering discipline to model serving.
Treating LLM systems like real software: building evaluation harnesses, golden datasets, and automated scoring to catch regressions and prove quality. Turning "it feels better" into measurable, reproducible results.
Rapidly prototyping and shipping my own products—using AI-assisted development to go from idea to working software fast. Learning new stacks from scratch and iterating in public.
Senior Software Engineer
Batching • Caching • Quantization
Experimenting with serving optimizations to cut LLM latency and cost for production workloads.
Golden Sets • LLM-as-Judge • Scoring
Building evaluation pipelines and benchmarks to measure LLM quality and catch regressions before they ship.
RAG • Agents • Evals
Rapidly building retrieval-augmented and agentic applications, from idea to shipped product.
0 → 1 • Full-Stack • AI
Building my own products end-to-end, accelerated with AI-assisted development.