Software engineer working at the intersection of applied AI, data systems, and backend engineering.
I build systems that turn complex data into useful, reliable software鈥攆rom computer vision on industrial edge devices to multimodal retrieval, streaming pipelines, graph analytics, and production Django applications.
I hold an M.S. in Computer Science from Arizona State University (3.90/4.00) and a B.Tech. in Information Technology from IIIT Bhubaneswar. My work is strongest where machine learning meets the engineering required to deploy, operate, and improve it.
- Applied AI: computer vision, multimodal retrieval, RAG, embeddings, and model inference
- Data systems: streaming, distributed processing, graph analytics, and data collection
- Backend engineering: Python services, Django, REST APIs, relational and vector storage
- Production infrastructure: Docker, Kubernetes, cloud services, CI/CD, monitoring, and deployment
| Project | What it does | Core stack |
|---|---|---|
| Multimodal PDF RAG Pipeline | Retrieves and answers questions over ArXiv papers using text and image embeddings. | Python, Django, CLIP, FAISS, RocksDB, Groq |
| NYC Taxi Graph Analytics | Processes streaming taxi data and derives graph insights with PageRank and BFS. | Kafka, Neo4j, Kubernetes, Docker, Python |
| Arizona Power Outage Archive | Collects hourly outage data from seven Arizona utilities using APIs and browser automation. | Python, REST APIs, Selenium, GitHub Actions |
| Django Blogging Platform | A modernized, tested, and deployed blogging platform with authentication and CI. | Django, PostgreSQL, Neon, Render, Bootstrap |
Languages Python 路 C++ 路 Java 路 JavaScript/TypeScript 路 SQL 路 Bash
AI / ML PyTorch 路 TensorFlow 路 OpenCV 路 scikit-learn 路 NumPy 路 Pandas 路 FAISS
Data systems Kafka 路 Spark 路 PySpark 路 Hadoop 路 Neo4j 路 PostgreSQL 路 RocksDB
Backend / web Django 路 Flask 路 REST APIs 路 React 路 Next.js 路 Node.js
Infrastructure Docker 路 Kubernetes 路 AWS 路 Google Cloud 路 GitHub Actions 路 Jenkins
- Delivered industrial computer-vision systems with 95%+ detection and tracking accuracy for enterprise manufacturing environments at Eternal Robotics.
- Improved edge inference performance by approximately 80% across Jetson-based deployments.
- Built a computer-vision PDF comparison workflow at Worley that reduced manual engineering drawing inspection time by approximately 65%.
- Supported graduate-level Data Processing at Scale coursework as an Instructional Assistant at Arizona State University.
Reliable AI systems, retrieval and search, data-intensive backend platforms, computer vision, and infrastructure that moves prototypes into production.
Tempe / Seattle 路 Open to relocation