Kudeshia Gaurav

Led end-to-end delivery of production AI solutions, owning discovery, architecture, implementation, evaluation, security review, rollout planning, observability, and post-launch support. Built multiple Python/FastAPI services and agent workflows using LangGraph and LlamaIndex, integrating enterprise data and documents through secure REST APIs. Designed RAG workflows across 12,000+ internal documents with access-aware retrieval, source grounding, structured responses, validation, and human review for low-confidence results. Productionized prototypes with input/output validation, exception handling, automated testing, logging and tracing, release checks, access controls, audit logging, and operational runbooks. Created evaluation plans and UAT with 25+ users; improved successful task completion from 68% to 86%. Diagnosed retrieval/workflow failures using logs, traces, evaluation results, and user feedback, reducing recurring production issues by ~25%. Partnered with data owners, applicat

Co-founded a 0-to-1 ML product that estimates an item’s future value from images and structured inputs. Defined MVP requirements, tested input quality, response time, explanations, confidence, edge cases, and result presentation, and iterated using prediction reviews and user feedback. Designed category-specific logic to handle sparse and inconsistent comparable-sale data while balancing reliability, latency, and privacy.

Translated loosely defined business workflows into technical requirements covering data availability, integrations, model behavior, security, evaluation, maintainability, and user experience. Mapped operational processes and system touchpoints to choose between AI, retrieval, predictive modeling, or simpler automation. Built proofs of concept for document review, enterprise search, predictive analysis, and workflow automation using Python, SQL, APIs, and retrieval techniques. Created reusable Python/API components for ingestion, validation, response formatting, exception handling, and downstream workflow integration. Built evaluation datasets with 400+ test cases; data-quality checks and structured experimentation improved pilot accuracy by 15 percentage points prior to technical handoff. Evaluated solution options across accuracy, security, operational complexity, maintainability, effort, and cost to help leaders select initiatives for continued development.