MS Data Science & Analytics AI Engineer Software Engineer

• 🌍 Based in San Jose, CA
• 💼 Building LLM-powered RAG systems, scalable data pipelines, and cloud-native backend infrastructure
• ✉️ Reach me at govardhanreddyb11@gmail.com
• 🧠 Hands-on experience across AI/ML, full-stack development, data engineering, and cloud deployments (AWS, Azure, GCP)

Connect with me!

Aug 2025 – Feb 2026 · Chico, CA

Python, LLM APIs, RAG, Snowflake, Vector DB, AWS, Prompt Engineering, Docker

• Architected an end-to-end RAG platform combining LLM APIs, vector database retrieval, and Snowflake-backed SQL transformations - enabling context-aware enterprise question answering over structured datasets at scale
• Designed and ran structured PoC experiments benchmarking zero-shot, few-shot, and retrieval-augmented prompting strategies; delivered findings as executive-level presentations that influenced adoption decisions
• Reduced production query latency by 35% through targeted SQL query optimization, schema redesign, and strategic indexing across high-volume analytical workloads
• Built real-time monitoring dashboards and KPI frameworks to track AI output quality, retrieval accuracy, pipeline throughput, and business metrics across the full system lifecycle
• Collaborated cross-functionally with faculty and technical panels to translate complex business data requirements into scalable, AI-powered solutions with clear, outcome-focused narratives

Jun 2025 – Aug 2025 · CA, USA

• LangChain, CrewAI, AutoGen, Azure, Python, SQL, CI/CD, Docker*

• Designed and deployed modular multi-agent AI systems using LangChain, CrewAI, and AutoGen to automate complex, multi-step SaaS operational workflows in Azure cloud environments
• Built ML-driven anomaly detection and behavioral drift monitoring systems that proactively surfaced model reliability risks before they impacted production pipelines
• Designed SQL-backed KPI dashboards tracking performance trends, error rates, and agent behavior patterns - giving engineering and product teams clear, real-time operational visibility
• Delivered CI/CD-ready containerized AI services via Azure pipelines, reducing deployment friction and accelerating iteration cycles across the engineering team
• Partnered with product and operations teams to translate ambiguous business requirements into measurable AI-backed solutions with defined success metrics

May 2023 – May 2024 · Bangalore, India

• Built and maintained data-driven backend services and relational database integrations for a centralized platform serving 500+ active daily users, ensuring uptime and data integrity
• Improved backend performance through SQL query optimization and schema-level redesign, reducing server response latency and increasing throughput for high-traffic user-facing endpoints
• Designed secure REST APIs with input validation and OOP-based architecture, establishing scalable patterns that supported consistent platform growth over the engagement
• Translated stakeholder requirements into maintainable, well-documented backend solutions; contributed stable production releases via Git-based code reviews with measurable quality outcomes
• Produced technical documentation and runbooks for deployment procedures and troubleshooting workflows, improving team onboarding speed and reducing resolution times Python Django SQL REST APIs AWS Git PostgreSQL

Apr 2022 – May 2023 · Bangalore, India

• Implemented CNN deep learning models using TensorFlow and Keras, achieving 92% validation accuracy on image classification tasks through systematic hyperparameter optimization and structured experimentation
• Collected, cleaned, and preprocessed 10,000+ image datasets using Python, NumPy, and OpenCV — improving training pipeline efficiency by 30% and ensuring high-quality model inputs
• Designed reproducible benchmarking pipelines measuring precision, recall, F1-score, and robustness across varied datasets, providing consistent baselines for model comparison
• Conducted systematic failure mode analysis to identify recurring error patterns and edge cases, driving targeted improvements that increased recognition performance by 15%
• Standardized data validation and preprocessing workflows to reduce inconsistencies and improve downstream model reliability across multiple training runs Python TensorFlow Keras NumPy Pandas OpenCV Model Evaluation

Feb 2022 – Apr 2022 · Bangalore, India

• Built large-scale NLP classification pipelines to process and categorize 20,000+ unstructured news articles using Python and SQL, achieving 94% classification accuracy across target categories
• Engineered automated data preprocessing and feature extraction workflows that reduced end-to-end model training pipeline runtime by 40%, significantly accelerating iteration cycles
• Designed rule-based evaluation frameworks to analyze classification variance, detect model drift, and flag inconsistencies in model outputs before deployment
• Integrated trained ML models into REST API backend services, enabling scalable, production-ready deployment and seamless consumption by downstream application teams
• Documented findings and evaluation results in structured reports, presenting insights to cross-functional stakeholders in clear, non-technical language Python NLP Machine Learning Neural Networks REST APIs SQL

Node.js · React · Python · AWS · Terraform · RAG Pipelines · Docker

• Built a RAG-powered chatbot that dynamically retrieves relevant content from course materials and generates precise, grounded answers — significantly reducing hallucinations compared to vanilla LLM responses
• Engineered a document summary extractor pipeline that condenses long-form learning content into structured summaries using LLM inference over context-relevant retrieved chunks
• Designed an interactive data dashboard surfacing learner engagement metrics and content performance KPIs in real time, enabling data-driven course improvement decisions
• Implemented modular quiz generation tied to retrieved content, enabling adaptive, personalized assessments grounded directly in actual course material
• Deployed scalable cloud-native infrastructure on AWS using Docker and Terraform with full CI/CD integration, enabling repeatable and production-ready releases

C++ · Python · Node.js · OpenCV · Docker · AWS

• Engineered a natural language command interface that parses voice inputs and maps them to system-level OS operations across Windows and Linux, enabling hands-free workflow automation
• Integrated OpenCV-based object detection modules to visually identify and interact with on-screen UI elements — automating tasks that lack accessible APIs or scripting hooks
• Designed a modular C++ execution engine with concurrent task handling, structured logging, and retry mechanisms to ensure deterministic, fault-tolerant command execution under varied system states
• Exposed REST API endpoints via Node.js to allow external systems and integrations to trigger automation workflows programmatically without direct UI interaction
• Containerized all components using Docker for consistent, reproducible cross-environment deployments with full execution traceability via structured audit logs

Python · SQL · REST APIs · AWS · Docker

• Developed Python-based algorithms to compute safety-critical metrics including collision probability, lane deviation, trajectory smoothness, and acceleration variance from large-scale simulation logs
• Built scalable SQL ETL pipelines to aggregate and transform high-volume structured and unstructured driving logs into clean, analytics-ready datasets for consistent metric computation at scale
• Implemented scenario-mining logic to automatically extract high-risk edge cases from large datasets - surfacing rare but safety-critical traffic events for targeted model evaluation and stress testing
• Designed automated reporting dashboards and REST APIs exposing scenario-level evaluation results, enabling engineering and product teams to make fast, data-driven safety decisions
• Structured the system around repeatable, modular experimentation workflows — making it straightforward to benchmark new driving policy models against a consistent baseline across diverse traffic scenarios

M.S. Data Science & Analytics — California State University, Chico (Aug 2024 – May 2026) Courses: Analysis of Algorithms · Advanced Data Science · AI · Applied ML / Deep Learning · DevOps Programming

B.E. Computer Science (AI & ML) — Christ University, Bangalore (Aug 2020 – May 2024) Courses: Algorithms · Data Structures · SQL/NoSQL · ML / Deep Learning · OOP · Compiler Design · Web & Android Dev

• AWS Certified Solutions Architect – Associate
• IBM Building AI Agents & Workflows
• Google Agile Project Management
• CISCO Python