🎓 M.S. Computer Science @ UMBC — GPA: 3.8 | August 2026
💼 3 years of industry experience at Tata Consultancy Services
🧑🏫 Graduate Assistant @ UMBC — mentored ~250 students across advanced CS courses
📍 Baltimore, MD | Open to relocation
🔎 Open to full-time Software Engineering & Machine Learning Engineering roles
I'm a software engineer and machine learning practitioner with 3 years of industry experience and a Master's degree in Computer Science from the University of Maryland, Baltimore County (UMBC).
My work sits at the intersection of AI/ML, backend engineering, and distributed systems. I enjoy taking ideas from experimentation to working systems — whether that's building retrieval-augmented AI applications, developing EEG-based machine learning pipelines, training self-supervised models for medical imaging, or designing concurrent and distributed services.
Before graduate school, I worked at Tata Consultancy Services, supporting backend systems, databases, cloud infrastructure, and CI/CD workflows for enterprise applications.
I'm particularly interested in:
- 🤖 Applied AI, LLMs & Retrieval-Augmented Generation
- 🧠 Machine Learning & Deep Learning
- ⚙️ Backend & Distributed Systems
- ☁️ Cloud Infrastructure & DevOps
- 🔬 AI for Healthcare & Human-Centered Computing
| Project | What I Built | Tech |
|---|---|---|
| 🤖 StudyBot — Retrieval-Augmented Study Assistant | RAG-based assistant that indexes course notes, retrieves relevant context, generates grounded answers, and refuses to answer when retrieval confidence is insufficient. Includes modular generation, evaluation, guardrails, and testing. | Python, RAG, Embeddings, Gemini, Pytest |
| 🧠 EEG Stress Detection — BCI Thesis | Research pipeline for real-time EEG-based stress detection using MUSE 2, designed as part of a closed-loop system for triggering CBT-based interventions. | Python, EEG, Signal Processing, ML, MUSE 2 |
| 🩻 Self-Supervised Learning for Medical Imaging | Explores SimCLR-based representation learning for chest X-rays, label efficiency, cross-dataset transfer, medical-specific augmentations, Grad-CAM, and embedding analysis. | PyTorch, SimCLR, ResNet, OpenCV |
| ⚙️ Concurrent & Distributed Prime Counter | High-performance prime-counting system with concurrent and distributed implementations using worker pools, goroutines, gRPC, and Protocol Buffers. | Go, gRPC, Protocol Buffers, Concurrency |
| 🌐 Time-Zone Scheduler API | Dockerized REST API for creating and managing timezone-aware events with automatic timezone conversion and input validation. | Python, FastAPI, Pydantic, Docker |
| 📝 Django Blog Platform | Full-stack web application with authentication, user profiles, CRUD operations, pagination, image uploads, and administrative functionality. | Python, Django, SQLite, Bootstrap |
Languages
Python · Go · Java · C++ · SQL
AI / Machine Learning
PyTorch · TensorFlow · scikit-learn · Deep Learning · Computer Vision · RAG · LLMs · Signal Processing
Backend Engineering
FastAPI · Django · REST APIs · gRPC · Microservices
Cloud & DevOps
AWS · Docker · Kubernetes · CI/CD · Jenkins · Git
Databases
MySQL · MongoDB · SQLite
Database Administrator & Systems Engineer | 3 years
- Supported enterprise backend and database systems for production applications.
- Worked across database administration, cloud infrastructure, incident resolution, CI/CD automation, and system reliability.
- Collaborated with cross-functional teams to troubleshoot production issues and improve operational workflows.
Graduate Assistant
- Mentored and supported ~250 students across advanced Computer Science courses.
- Covered subjects including Artificial Intelligence, Algorithms, Machine Learning, Computer Networks, Distributed Systems, Network Security, Quantum Information Science, Information & Coding Theory, and performance optimization.
- Assisted with technical mentoring, debugging, grading, examinations, and implementation-focused problem solving.
I'm currently exploring how machine learning systems can move beyond notebooks into reliable, real-world applications.
Some areas I'm actively working on include:
- Building reliable RAG and LLM-powered applications
- EEG-based machine learning and brain-computer interfaces
- Self-supervised learning for medical imaging
- Backend architecture and distributed systems
- Production-oriented ML engineering
- 🧠 Improving my EEG stress-detection and BCI research pipeline
- 🤖 Building and evaluating applied AI/RAG systems
- ⚙️ Strengthening backend and distributed-systems engineering
- 🧩 Practicing data structures, algorithms, and system design
- 🚀 Building production-quality projects for SWE and MLE roles
I'm currently open to Software Engineer, Backend Engineer, and Machine Learning Engineer opportunities.
Interested in building intelligent systems that are useful, reliable, and engineered for the real world.