# Raghavendra Ganugapati > GenAI Engineer @Cognizant | Actively looking for FTE / C2C /C2H / W2 | Building Production LLM Systems with Multi-Agent Architectures | Python • AWS• Azure Location: San Francisco Bay Area, United States Profile: https://flows.cv/raghavendraganugapati AI & GenAI Engineer with 4+ years building production-scale agentic AI systems and enterprise ML platforms across healthcare, finance, and retail. Proven delivery of measurable business impact 🔹 Agentic AI & Multi-Agent Platforms: Architected production agentic systems using LangGraph with specialized agents, conditional routing, and tool-calling capabilities — enabling dynamic invocation of CRM APIs, knowledge bases, and escalation workflows with zero rigid rule-based logic. 🔹 MCP Integration & Enterprise Connectivity: Designed and deployed Model Context Protocol (MCP) servers connecting LLM agents to enterprise systems with sub-200ms latency and real-time context refresh for customer data, order history, and internal documentation. 🔹 RAG & Vector Retrieval Systems: Built hybrid search pipelines (dense embeddings + BM25 sparse retrieval) using Pinecone with optimized chunking, overlap tuning, and reranking strategies for high-relevance, low-latency knowledge retrieval. 🔹 MLOps & Production AI Deployment: Deployed Dockerized FastAPI microservices on AWS EKS with Redis semantic caching, intelligent LLM routing (GPT-4o / GPT-4o-mini), canary deployments, and full observability via Prometheus/Grafana. 🔹 Data Quality & LLMOps: Engineered automated monitoring pipelines tracking schema drift, token distribution shifts, and retrieval quality degradation — reducing debugging time by 60% through proactive alerting and root cause analysis. 🔹 Cloud & Distributed Systems: End-to-end delivery on AWS and Azure (SageMaker, EKS, Azure ML, Databricks) with CI/CD for ML, MLflow experiment tracking, and scalable ETL pipelines processing millions of records daily . 📩 Open to connecting on agentic AI, RAG system design, MCP tooling, LLMOps, and production GenAI engineering. ## Work Experience ### Generative AI Engineer @ Cognizant Jan 2024 – Present | California, United States In this role, I design and deploy production-grade AI systems centered around large language models, agentic workflows, and tool-using agents. My responsibilities include architecting end to-end GenAI solutions, building retrieval-augmented generation pipelines over enterprise data, and integrating LLMs with internal APIs, databases, and automation services. I work extensively with Python, containerized services, Kubernetes, and cloud infrastructure to ensure these AI systems are secure, scalable, and maintainable. A key part of my role involves translating ambiguous business needs into deployable AI solutions while collaborating closely with product and engineering teams. I have delivered LLM-powered systems that reduced manual analysis and decision workflows by over 40% across internal teams. I improved response accuracy and reliability by implementing RAG pipelines and prompt evaluation mechanisms that materially reduced hallucinations in real usage. I productionized AI services as low-latency APIs with enforced cost controls and access isolation, and introduced monitoring loops for prompt, agent behavior, and failure analysis. My work established reusable internal patterns for safely deploying agentic AI systems at scale. ### Graduate Assistant @ Clemson University Jan 2023 – Jan 2024 | Clemson, South Carolina, United States ### Graduate Research Assistant @ Clemson University Jan 2023 – Jan 2023 | United States ### AI Engineer @ Deloitte Jan 2021 – Jan 2022 At Deloitte, I worked on building and productionizing machine learning–driven data pipelines that supported both analytical and operational use cases. My role focused on designing feature-ready data models, building scalable Python and SQL pipelines, and ensuring data reliability for downstream ML workflows. I collaborated closely with data scientists to operationalize experimental models into stable production assets, ensuring they could be consumed reliably by business stakeholders. I enabled faster model iteration cycles by designing clean, feature-ready transformations and integrating ML outputs into dashboards and downstream systems. I improved reliability by implementing data validation and monitoring within pipelines, reducing downstream reporting issues. My work helped teams transition from experimental ML efforts to production-ready, enterprise-grade solutions that delivered consistent insights within existing workflows ### Associate Software Engineer @ Accenture in India Jan 2021 – Jan 2021 | India In my early career at Accenture, I built and automated backend workflows using Python and SQL, supporting data pipelines and recurring client deliverables. I worked on developing and maintaining production systems that handled multi-source inputs, ensuring consistent data availability for analytics and reporting use cases. This role strengthened my foundation in software engineering, modular design, and production accountability. I reduced manual processing through automation, improved pipeline reliability, and delivered cloud-deployed solutions under real client timelines. I built reusable utilities, standardized ingestion logic, and collaborated across teams to ship systems used by real clients. This experience laid the groundwork for my transition into AI and GenAI system ownership roles. ## Education ### Masters in Computer Science Clemson University ### Bachelor of Engineering - BE in Electronics and Communication Osmania University ### Junior College MPC Sri Chaitanya College of Education ### 10th class Kendriya Vidyalaya ## Contact & Social - LinkedIn: https://linkedin.com/in/raghavendra-niteesh --- Source: https://flows.cv/raghavendraganugapati JSON Resume: https://flows.cv/raghavendraganugapati/resume.json Last updated: 2026-04-18