Hiteshee Rathod

About Hiteshee Rathod

I am a Software Development Engineer with 5+ years of experience building production-grade platforms across financial services and insurance domains. Currently working on AI Platforms, I design and deploy GenAI and Retrieval-Augmented Generation (RAG) systems that transform complex regulatory and credit workflows into scalable automation solutions. At Morgan Stanley, I develop Python-based AI services leveraging FastAPI, LangChain, vector search, and OpenAI APIs to automate document intelligence and risk analysis workflows. My work focuses on building reliable, evaluation-driven LLM systems with strong guardrails, structured outputs, and enterprise-grade deployment across AWS environments. Previously, at Cognizant (Hartford Financial), I engineered backend systems supporting policy issuance, claims processing, and regulatory reporting platforms. I optimized SQL systems, migrated legacy batch jobs into Python ETL pipelines, and improved production reliability while maintaining strict SLA and compliance standards. My core strengths include: • GenAI & RAG Systems (LangChain, Vector Search, Embeddings) • Python Backend Engineering (FastAPI, REST APIs, Async Systems) • Cloud & Develops (AWS, Docker, Kubernetes, CI/CD) • SQL Engineering & Data Systems Optimisation • Enterprise AI Deployment with Monitoring & Guardrails • Financial & Insurance Workflow Automation I specialise in translating complex regulatory and operational requirements into scalable AI-driven platforms that improve efficiency, reduce manual effort, and enhance decision intelligence.

Experience

1. Software Engineer Ai Platforms

   Morgan Stanley

   Aug 2025 — Present

   Developed Python-based GenAI services using FastAPI and OpenAI APIs to automate credit and regulatory documentanalysis workflows, reducing manual review effort by 38% across underwriting and compliance operations.• Designed retrieval-augmented generation (RAG) pipelines using LangChain, vector embeddings, and FAISS indexing toenable contextual search across large financial policy and deal documentation repositories.• Engineered scalable data ingestion and preprocessing pipelines integrating SQL and cloud storage sources, enablingstructured and unstructured financial datasets to support AI-driven decision workflows.• Deployed containerized AI services using Docker and CI/CD pipelines across AWS environments, enabling controlledreleases, rollback capability, and consistent model delivery into enterprise production systems.• Implemented evaluation and monitoring frameworks using prompt testing and response validation techniques to improveLLM answer relevance and reduce hallucination risk within regulated financial workflows.• Collaborated with product owners, risk analysts, data engineering teams in Agile delivery cycles to translate investmentbanking and risk management requirements into deployable AI solutions supporting operational automation initiatives.

Education

• Government College of Engineering And Research,Pune

  Bachelor of Engineering - BE, Computer Engineering

  2015 — 2019

• Syracuse University

  Master of Science - MS, Computer Science