I build and scale end-to-end AI systems that move beyond experimentation into real-world, high-reliability production environments. My work focuses on architecting intelligent systems powered by large language models, retrieval-augmented generation (RAG), and agentic workflows, with strong emphasis on performance, observability, and cost-efficient scaling.
I have deep experience engineering backend systems in Python (FastAPI/Flask), designing cloud-native architectures on GCP, and delivering robust cross-platform applications using React Native (Expo). My engineering approach prioritizes system design, scalability, and reliability over isolated model experimentation.
My core expertise includes LLM integration, AI orchestration, retrieval systems, vector search, and production AI infrastructure. I specialize in identifying and solving bottlenecks across AI pipelines—from retrieval quality and latency optimization to prompt and context engineering.
I operate at the intersection of software engineering and applied AI, building systems that transform state-of-the-art AI capabilities into dependable, production-ready products at scale.
TypeScript, JavaScript, Python, Java, SQL
React Native (Production Experience), ReactJS, Next.js, NodeJS, PostgreSQL, REST/GraphQL APIs, UI/UX
Flask, Django, FastAPI, NestJS (Self-Taught/Familiarity), Chatbot Development, Recommendation Systems, NLP, Model Optimization
Git, GitHub, CI/CD with GitHub Actions, TDD, AI-Assisted Development Workflows (Proven ability to adopt new tools like Cursor IDE), GCP, AWS (basic exposure), MLOps fundamentals, Prisma (ORM - Self-Taught/Familiarity)
