Building scalable ML platforms, distributed training systems, and AI infrastructure. Expert in MLOps, model optimization, and production-grade machine learning.
I am a Machine Learning Systems Engineer with deep expertise in building production-grade AI infrastructure. My work spans from distributed training systems to edge deployment, with a focus on scalability, efficiency, and reliability.
Large-scale model training across GPU clusters with optimized data parallelism and model parallelism strategies.
Building robust ML platforms with Kubernetes, Kubeflow, and custom orchestration for production workloads.
High-throughput data processing pipelines with Apache Spark, Ray, and modern data lake architectures.
Multi-cloud deployments on AWS, GCP, and Azure with focus on cost optimization and scalability.
Quantization, pruning, and distillation techniques for deploying efficient models at the edge and cloud.
End-to-end ML lifecycle management with CI/CD, monitoring, and enterprise-grade security practices.
Technical blog posts, paper reviews, and tutorials on ML systems, infrastructure, and distributed computing.
Most developers use Claude Code at 20% of its potential. These 13 workflows -- from parallel instances to feedback loops -- will change how you ship. Fork the repo, work through the exercises, and build muscle memory by end of day.
Read moreI benchmarked three open-source NL2SQL approaches against the same DuckDB database with 50 gold-standard queries. Vanna AI's RAG approach led at 40% execution accuracy, but all three tools reveal hard truths about the state of text-to-SQL.
Read moreA deep technical teardown of three leading open-source NL2SQL frameworks -- tracing the query path from natural language to SQL, comparing RAG vs semantic models vs multi-agent architectures, and identifying what works and what doesn't.
Read moreA deep dive into root-causing a multi-prefill disaggregation bug in SGLang's NIXL backend -- from symptoms to a three-component failure spanning Rust, Python, and ZMQ.
Read moreA comprehensive, evolving guide to effectively using Opencode for AI-powered coding. Learn tips, tricks, and workflows from daily use that will transform your development workflow.
Read moreA structured learning path covering production ML systems — from distributed training to LLM serving at scale.
4 modules · 1 lesson
Whether you are looking for a senior ML Systems engineer, need consulting on AI infrastructure, or want to collaborate on research, I would love to hear from you.