Generative AI Engineer · M.S. Computer Science @ ASU
Building AI systems that survive production
Agentic AI · RAG · Multimodal systems · Evaluation · Cloud delivery
I build AI systems that save time, reduce cost, and hold up when real users touch them.
Currently targeting full-time new-grad roles in software engineering, AI/ML engineering, and machine learning for 2027.
| 20+ production AI apps |
~80% faster document retrieval |
~30% lower inference cost |
99.3% virtual patient accuracy |
I am a Generative AI engineer and researcher pursuing an M.S. in Computer Science at Arizona State University (GPA: 3.94/4.00). I work across agentic systems, RAG, multimodal AI, backend infrastructure, and evaluation. At IBM, I shipped enterprise AI systems for real client workflows. At ASU, I now work across applied research and technical leadership in healthcare and operational AI.
My favorite problems sit at the intersection of language models, useful software, measurable outcomes, and deployment reality.
| Focus | What it looks like |
|---|---|
| Agentic automation | Contract and research workflows with LangGraph, LangChain, and document intelligence |
| Health AI | Passive sensing with GPS, Bluetooth, Wi-Fi, and accelerometer data |
| Production GenAI | Observability, evaluation, cost control, APIs, containers, and Kubernetes |
| Multimodal systems | Connecting text, behavioral signals, and real-world outcomes |
| When | Where | What I worked on |
|---|---|---|
| 2026 - now | ASU Edson College | AI Integration Engineer on a $5.2M DoD-funded virtual patient project; redesigned RAG and agentic workflows to improve response accuracy from 70.5% to ~99.3% |
| 2026 - now | ASU VISA Research Lab | Graduate Research Assistant building React Native/Kotlin mobile sensing apps and cloud AI pipelines for real-time health metrics |
| 2023 - 2025 | IBM | AI Engineer delivering 20+ production AI applications, 4-6 week client PoCs, RAG systems, agentic workflows, and cloud-native deployments |
Python Java SQL JavaScript C++
LangGraph LangChain CrewAI RAG MCP WatsonX PEFT / LoRA
FastAPI Flask Docker Kubernetes React Native Kotlin
AWS: EC2 / S3 / DynamoDB IBM Cloud PostgreSQL MongoDB ElasticSearch Neo4j Firebase
React Streamlit HTML CSS Bootstrap D3.js Plotly Power BI
Granite Llama Vector Embeddings Prompt Engineering Postman Swagger CloudWatch Git
- IBM prior-art publication describing a retrieval-augmented, generation-based multimodal Q&A solution
- Published research in
IJSREM - Applied AI research across passive sensing, psychophysiology, clinical neuro AI, and virtual humans
- Cloud-Native Web Tier - AWS multi-tier architecture with EC2 Auto Scaling, an Application Load Balancer, S3, DynamoDB, IAM least privilege, and CloudWatch observability
- Autonomous robot path planning and reinforcement learning using ROS, BFS, UCS, GBFS, A*, and Q-Learning
- Graduate admission predictor comparing Linear Regression, SVR, Decision Trees, and Random Forest models