Gen AI Engineer
I build intelligent systems that work in production — RAG pipelines, MCP servers, and LLM-powered agents that cut retrieval time by 40% and hit 99%+ accuracy.
About
I started my career ensuring software worked flawlessly — 13+ years of relentless attention to systems, data, and outcomes. Now I build the systems themselves. Today at Cisco, I design and ship RAG pipelines, MCP servers, and LLM-powered agents that move the needle.
My MS in Business Analytics from Clark University (GPA 3.96) gave me the analytical foundation; years in production gave me the instincts. I was inducted into Beta Gamma Sigma — the top 20% of business school graduates worldwide — for academic excellence.
I build things that are accurate, fast, and explainable. Not demos. Production-grade AI systems that engineers and stakeholders can trust.
Education
MS Business Analytics, Clark University
Academic Standing
GPA 3.96 · Beta Gamma Sigma
Current Role
AI Engineer / Test Lead at Cisco
Open To
Gen AI / MLRoles
Capabilities
End-to-end AI systems — from retrieval architecture to deployed models.
Production retrieval-augmented generation systems with optimized chunking, embeddings, and hybrid vector search.
Model Context Protocol servers that expose structured tools and enterprise data to LLM agents at scale.
Autonomous LLM-powered agents with tool use, memory, and multi-step reasoning for complex enterprise workflows.
Supervised and ensemble models for fraud detection and prediction — built to achieve 99%+ accuracy in production.
Career
13+ years across enterprise tech, building systems at every layer of the stack.
Cisco Systems
AI Engineer / Test Lead · San Jose, CA
Built and deployed a RAG pipeline using LangChain + FAISS, indexing 50+ documents — reducing engineer retrieval time by 40%. Optimized RAG retrieval precision by 30% through custom chunking, metadata filtering, and embedding tuning. Designed and shipped 3 MCP servers in Python, enabling LLM agents to autonomously call internal Cisco APIs and tools, saving ~5 hrs/week across the team. Applied prompt engineering and agentic AI patterns to automate test scenario generation and intelligent test prioritization. Led enterprise E2E QA delivery across 20+ cross-functional teams, achieving 100% cross-system data reconciliation via SQL.
Sri Infotech, Inc.
Data Analyst · Boston, MA
Cleaned and analyzed 50k+ record datasets using Python and SQL. Built automated Power BI dashboards delivering actionable insights to business stakeholders.
Infosys (Client: Cisco)
Technical Test Lead · Hyderabad, India
Automated 70% of regression tests using Java, Selenium WebDriver (POM), TestNG, and Maven — significantly accelerating release cycles for an enterprise AngularJS application. Reduced defect leakage by 30% and defect turnaround time by 20% through structured testing. Delivered executive-level QA reporting to senior stakeholders.
IGT Pvt Ltd
SQA Engineer II · Hyderabad, India
Performed functional, performance, and API testing using Postman and Rest Assured. Identified 50+ critical defects, reducing post-release issues by 20% and improving test reliability by 25%.
ADP India Pvt Ltd
Member Technical · Hyderabad, India
Delivered quality validation across PeopleSoft HCM modules. Authored 300+ test cases and introduced Java/Selenium automation, reducing manual testing effort by 30%.
Work
Production systems, not side projects. Built to measure.
Built and deployed a RAG pipeline using LangChain + FAISS, indexing 50+ internal Cisco documents. Replaced manual knowledge-based searches with LLM-grounded responses via OpenAI API. Further optimized retrieval precision by 30% through custom chunking strategies and embedding tuning.
⚡ 40% reduction in retrieval time · 30% precision gainBuilt 3 production MCP servers exposing enterprise data sources, APIs, and compute resources as structured tools for LLM agents. Enables autonomous workflows across Cisco's AI platform infrastructure.
🚀 3 servers in productionBuilt ML models to predict fraud and late delivery in supply chain data. Used Logistic Regression, Random Forest, and Decision Tree with PySpark + scikit-learn across a large-scale dataset. Performed end-to-end feature engineering and model evaluation.
🎯 99.12% model accuracyApplied logistic regression to detect fraudulent payment transactions. Performed end-to-end data exploration, cleaning, and model evaluation on financial services data to minimize false positives while maintaining near-perfect recall.
🎯 99.91% model accuracyToolkit
Technologies I use to build intelligent systems end-to-end.
Background
Graduate Degree
Master of Science in Business Analytics
Certification
PCEP — Certified Entry-Level Python Programmer
Certification
Google Analytics Certification
Contact
Open to Gen AI/ML engineering roles, research collaborations, and advisory conversations. If you're working on something serious, let's talk.