Build useful, secure, and measurable AI products with an AI engineer experienced in retrieval-augmented generation, large language models, LangChain, OpenAI APIs, vector databases, and full-stack product integration.
A production AI application needs more than a prompt. I design retrieval pipelines, document ingestion, chunking, embeddings, vector search, context assembly, model orchestration, citations, and permission-aware responses. The result is a maintainable RAG system that connects language models to trusted business knowledge while reducing hallucinations.
AI features can be integrated into React, Next.js, Node.js, Java, and Spring Boot applications without replacing reliable business systems. Typical integrations include intelligent search, document question answering, conversational support, text-to-SQL, summarization, workflow automation, and structured data extraction.
Production AI engineering includes authentication, data privacy, prompt-injection defenses, rate limits, caching, model fallbacks, evaluation datasets, cost monitoring, and human review. Cloud deployment on AWS can support scalable APIs, background processing, vector storage, and application observability.
