I'm an AI/ML engineer doing my Master's in Computer Science at the University of North Texas (GPA 3.8), and most of my work lives at the intersection of LLM engineering, computer vision, and making AI systems actually reliable in production.
I don't like stopping at proof-of-concept. Most of what's in this GitHub is end-to-end — agents that run autonomously, tracking systems that stream to live dashboards, platforms that non-engineers can actually use, and observability pipelines that catch problems before users do.
Before grad school I spent 15 months as an Applied Scientist intern at CDK Global, working on ML systems for one of the largest automotive retail platforms in the US. That's where I learned what production ML actually demands. I also published a facial recognition research paper at a Springer international conference during undergrad.
When I'm not building things: cricket, gym, and the occasional sketch.
| Area | Tools |
|---|---|
| Large Language Models | GPT-4 · Gemini · LLaMA · Google Gemma · Ollama |
| Agentic AI | LangGraph · Multi-agent workflows · Tool-augmented LLMs |
| RAG & Retrieval | FAISS · Schema-grounded RAG · Embedding pipelines · Document chunking |
| LLM Observability | Arize · Vertex AI evaluation · LLM-as-a-judge |
| Computer Vision | YOLOv8 · Deep SORT · OpenCV · dlib · MTCNN · EasyOCR · face-recognition |
| Deep Learning | PyTorch · TensorFlow · Keras · 3D CNN · Transfer Learning (EfficientNet) |
| Classical ML | Scikit-learn · XGBoost · LBPH · NumPy · Pandas |
| Cloud — GCP | Vertex AI · AutoML · Cloud Run · BigQuery · Artifact Registry · Secret Manager |
| Cloud — AWS | SageMaker · S3 · IAM · ECR |
| MLOps | Docker · Kubernetes · Terraform · GitHub Actions CI/CD |
| Backend | FastAPI · Streamlit · REST APIs · WebSocket |
| Databases | PostgreSQL · MySQL · FAISS · Apache Spark · SQL |
| Languages | Python · Java · SQL |
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A LangGraph state machine that handles the full GitHub issue lifecycle without human intervention — classification, assignment, SLA enforcement, audit logging, and auto-close on stale issues. Business impact: Returns 5–10 hrs/week of engineering overhead back to the team.
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Polls LLM trace data from Arize every minute, evaluates each response with a Vertex AI judge model, and deploys the full pipeline on GCP Cloud Run via Terraform. Multi-cloud: built on AWS SageMaker, runs on GCP. Business impact: Catches model degradation in minutes, not when a customer complains.
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Schema-grounded RAG pipeline that lets anyone query a PostgreSQL database in plain English. The LLM receives live table definitions, foreign keys, and business rules before generating SQL. Runs entirely locally with Ollama + Gemma — no data leaves the machine. Business impact: Answers in 30s what used to require a Jira ticket and a day's wait.
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Full-stack vending operations platform. OCR pipeline reads supplier receipts and updates inventory automatically. Rolling 7-day demand forecast flags machines before they stock out. Profit calculated from actual invoice costs, not estimates. Business impact: Eliminates manual data entry + targets 15–25% revenue lost to stockouts.
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Upgraded an open-source tracking system — replaced Darknet/TF 1.14 with YOLOv8, fixed a concurrency bug by giving each camera its own Deep SORT instance, and added vehicle intelligence: color detection, plate OCR, and type classification. Business impact: One operator monitoring 10+ live feeds with automated event detection.
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Two detection pipelines (Haar Cascade + MTCNN), LBPH recognition, and real-time color-coded alerts based on criminal record lookup. Undergraduate thesis published at BVRITHCON-2023 (Springer). Research: Compared traditional vs deep learning face detection on live video.
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Ongoing Research: CNN-Based Autism Detection via 4D fMRI — 3D CNN on resting-state neuroimaging to classify ASD vs. neurotypical subjects. Research project at UNT. (Repo coming soon)
| Institution | Period | Result | |
|---|---|---|---|
| MS Computer Science | University of North Texas, TX | 2024 – Present | GPA 3.8 / 4.0 |
| BE CSE (AI & ML) | GIET, JNTU Kakinada, India | 2020 – 2024 | CGPA 8.0 / 10 |
Published Research
Efficient Person Identification using Artificial Neural Networks International Conference BVRITHCON-2023 · Published by Springer doi.org/10.1007/978-981-95-0144-1_25
Certifications
- Microsoft Azure AI Engineer Associate — Microsoft (Apr 2023)
- AWS Academy: Machine Learning Foundations — Amazon Web Services (Jan 2023)
- AWS Academy: Cloud Architecting — Amazon Web Services (Jan 2023)
- AWS Academy: Cloud Foundations — Amazon Web Services (Nov 2022)
- Python for Data Science — IBM (Jun 2023)
- MTA: Introduction to Programming Using Python — Microsoft (Jun 2022)
I'm actively looking for AI/ML engineering roles — full-time or internship. If you're working on something in LLMs, agentic AI, computer vision, or MLOps, I'd love to connect.
