Professional Overview
I architect RAG pipelines and multi-agent AI systems that give 500+ engineers natural language access to 500K+ documents — vehicle designs, powertrain data, regulatory specs, and technical manuals — reducing knowledge retrieval from hours to seconds. I built a LangChain-powered clinical AI assistant at Kroger that delivered a 52x reduction in phar
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Architected enterprise-scale AI ingestion pipelines processing 500K+ engineering
- Built production-grade RAG pipelines on LangChain and LangGraph, enabling 500
- Designed and deployed a multi-agent AI system with four specialized expert agent
- Developed ML data pipelines that consume pharmacy claims, EHR data, prescription
- Created RAG-based clinical knowledge assistants based on LangChain
Education
Field of Study: Computer Science
Areas of Expertise
AI & Machine Learning Engineers
Design, train, and ship production ML models — from data pipelines to model inference at scale. Covers supervised learning, neural networks, LLM integration, and model optimisation.
MLOps & AI Infrastructure
Architect scalable ML infrastructure — CI/CD for ML, model versioning, experiment tracking, monitoring, and cloud deployment on AWS, GCP, or Azure.
Data Science & Analytics
Extract business insights from raw data using statistical modelling, predictive analytics, A/B testing, and interactive dashboards.
Automation & AI Workflows
Build intelligent automation pipelines, integrate AI APIs into existing tools, and replace manual workflows with agentic AI systems.
