Sagar T.

I help teams turn LLM prototypes into reliable production systems through RAG evaluation, tool-using agents, and deployment-ready Python APIs. The goal: fewer silent failures, clearer quality gates, and systems your team can operate. My Upwork track record includes a 445-hour AWS engagement, scientific language-model deployment on Google Cloud Vertex AI, core LLM/Transformer development, OpenAI NLP integration, Claude Code/MCP setup, and production API work. Clients have described my work as “smart, fast, effective” and highlighted my research, communication, and reliability. I can help with: • RAG architecture and evaluation: retrieval quality, groundedness, structured-output validation, regression tests, and LLM-as-judge workflows • AI agents and MCP: tool calling, approvals, retries, tracing, guardrails, and failure recovery • LLM APIs and deployment: Python, FastAPI, Docker, AWS, GCP, authentication, monitoring, and backend integration • Fine-tuning experiment design: LoRA/QLoRA configuration, dataset preparation, reproducible training plans, and before/after evaluation • Document AI: OCR, transformers, classification, hybrid search, and API delivery I start with a measurable baseline, document assumptions, separate tested results from targets, and leave teams with reproducible systems and operating guidance. If you need an LLM system evaluated, hardened, deployed, or made easier to operate, send me the architecture and the failure mode you want to solve.