I am an AI/ML Engineer with 5+ years of experience building and deploying production-scale machine learning systems. My work spans search, recommendation, computer vision, and generative AI, with a strong focus on delivering measurable business impact.
I specialize in LLM-powered applications, including GPT-4-based workflows, retrieval-augmented generation, semantic retrieval, embeddings, prompt engineering, and agentic automation. I enjoy turning complex product needs into scalable AI solutions that can operate reliably in production.
At Uber, I architected deep-learning ranking models and personalized ranking pipelines that improved discovery, conversion, and search relevance at massive scale. I also built semantic retrieval and RAG systems, real-time feature pipelines, and low-latency inference services supporting multiple product teams.
Previously at Walmart Global Tech, I developed ranking and recommendation models for sponsored advertising, built ML inference microservices, and improved model freshness through automated retraining pipelines. I also worked on computer vision systems and evaluated LLM approaches for semantic retrieval and query understanding.
My background includes strong experience with Python, SQL, TypeScript, JavaScript, React, PyTorch, TensorFlow, FastAPI, AWS, Docker, Kubernetes, Kafka, Spark, PostgreSQL, Elasticsearch, Redis, and modern ML tooling. I am comfortable working across the full lifecycle of AI systems, from experimentation and model development to deployment and monitoring.
I have a PhD in Electrical and Electronics Engineering from the University of California, Riverside, along with a master's degree from Washington University in St. Louis and bachelor's degrees from Swansea University and North China Electric Power University. I value cross-functional collaboration and data-driven experimentation to build AI products that create real user value.
