AI Engineer | Agentic Systems | Enterprise RAG | LLM Infra
Building scalable, compliant AI systems for real-world financial deployment. Specializing in RAG, multi-agent workflows, and LLM infrastructure on Azure & AWS.
About me
I'm an AI Engineer with 2+ years shipping production LLM systems at enterprise scale — real systems handling terabytes of regulated financial data with GDPR/SOX compliance.
At Northern Trust I work at the intersection of cutting-edge AI and strict enterprise requirements: RAG architectures, multi-agent systems, sub-100ms latency, and bias detection at scale.
Beyond the code, I served as Chapter President of NSLS at Roosevelt University — leading 100+ members through leadership development and bridging academic learning with industry AI applications.
I'm actively looking for my next AI Engineer role. Based in Chicago — open to remote and relocation.
Experience
Skills
A comprehensive overview of my skills across AI/ML, cloud, and software engineering.
Projects
Architected and deployed a scalable RAG system on Azure AI platform powering a member-facing chatbot at Northern Trust. Handles 10+ TB of GDPR/SOX-compliant financial data with sub-100ms response times and bias detection.
Engineered a PoC multi-agent system using AutoGen and LangGraph to automate internal document processing at Northern Trust — achieving 60% reduction in manual operational effort with seamless enterprise integration.
Built an enterprise-grade product matching engine using FAISS embedding similarity and transformer fine-tuning. Achieved 92% accuracy on 1M+ retail records. Streamlit HITL interface improved annotation efficiency by 50%.
Production-ready LSTM forecasting system with walk-forward validation achieving 3.8% MAPE and 25% improvement over baselines. Deployed via FastAPI + Docker with full CI/CD pipeline and monitoring.
Achievements
Highlights of impact, recognition, and continuous growth in AI engineering and leadership.
Architected and deployed enterprise-grade RAG system handling 10+ TB financial datasets with GDPR/SOX compliance and sub-100ms response times in production.
Pioneered agentic AI solutions using AutoGen and LangGraph, demonstrating 60% reduction in manual operational effort through intelligent multi-agent automation.
Built production-ready ML systems with Docker, Kubernetes, CI/CD pipelines, and comprehensive monitoring — achieving enterprise-grade reliability and observability.
Led 100+ student members as NSLS Chapter President while maintaining enterprise AI engineering responsibilities — bridging academic learning with industry AI applications.
Education
Contact
Based in Chicago — open to remote and relocation.