I transform fragmented enterprise data into high-precision, searchable intelligence. With a foundation as a Senior Software Engineer, I specialize in architecting Production-Grade RAG (Retrieval-Augmented Generation) systems that allow Large Language Models to interact securely, accurately, and cost-effectively with private datasets.

I don't just wrap APIs; I build the entire data lifecycle. Whether it’s optimizing high-performance vector ingestion pipelines or building intuitive, modern UIs to surface insights, I ensure your AI is grounded in truth, scalable in production, and seamlessly integrated into your existing tech stack.

Technical Strategy & Expertise:
- Advanced RAG Architecture: Implementing Hybrid Search (Vector + Keyword), Semantic Re-ranking, and Parent-Document Retrieval to eliminate hallucinations and maximize retrieval accuracy.
- Full-Stack Engineering: Expert implementation in Python (FastAPI/Django), Go, Spring boot and TypeScript to build the robust backends and responsive frontends (React/Next.js) required for AI scale.
-Vector Infrastructure: Designing and optimizing high-dimensional data storage using pgvector, Pinecone and Milvus.
- Data Ingestion (ETL): Building automated pipelines to clean, chunk, and embed unstructured data (PDFs, Documentation, SQL Databases) for real-time AI access.
- Evaluation & ROI: Using tools like LangSmith and RAGAS to provide quantitative proof of accuracy, latency, and token-cost efficiency.

I bridge the gap between Raw Data and Actionable Insights. Let’s build an AI infrastructure that your business can actually trust.