🎓 MS in Data Science — Indiana University, Bloomington
🤖 AI Engineer — fine-tuning LLMs, building agents & production RAG systems
📊 3+ years in Data Analytics, BI & risk modeling (the foundation)
🔬 Currently: QLoRA fine-tuning, RLVR reasoning eval, LLM inference optimization
🎯 Goal: engineer intelligent systems that automate real decisions
⚡ Fun fact: I still believe data cleaning is the most critical step in AI success
I build AI systems that act, not just analyze. Today that means fine-tuning open LLMs, designing agentic workflows, shipping retrieval-augmented generation into production, and squeezing real latency and throughput out of model serving.
But I didn't start in AI Engineering — I started by asking the right questions of data. Years of cleaning messy datasets, writing complex SQL, building KPI dashboards, and running statistical analysis taught me how data actually behaves. That intuition is exactly what makes the AI work reliable: good models live or die on good data.
The analyst in me asks "what does the data say?" The engineer in me builds systems that answer autonomously.
🔍 What drives me
- LLM Fine-Tuning — QLoRA / PEFT on domain datasets, measured against held-out evals
- Agentic AI — multi-agent systems (LangGraph, CrewAI, MCP) that reason and execute
- Advanced RAG — retrieval architectures with re-rankers and vector search for precision at scale
- LLM Reliability — RLVR reasoning evaluation, inference optimization, hallucination control
- Production Deployment — FastAPI, Docker, AWS, Terraform infrastructure-as-code
- Data-Driven Decisions — EDA, statistical testing, KPI dashboards, storytelling with data
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🧪 LLM Fine-Tuning (QLoRA)
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🧠 Digital-Twin Research Assistant
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🔁 RLVR Reasoning Evaluation
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🤖 Multi-Agent & RAG Systems
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SQL querying, KPI dashboarding, EDA, statistical testing, cohort analysis, and stakeholder storytelling.
"Started by reading the data. Now I build the systems that read, reason, and act on it for you."