🎓 Master's in CS @ University at Buffalo | 🤖 AI/ML Enthusiast & SWE Explorer
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I build intelligent systems — from scientific RAG pipelines to medical diagnostics,
and love turning deep learning + Software engineering into real-world applications.
- 🚀 I enjoy building production-grade AI systems and developer-focused tools with measurable real-world impact
- 💡 Passionate about the intersection of LLMs, RAG, multimodal learning, and software engineering
- 🧰 Equally confident in training deep models or scaling fast APIs and databases
- 💼 Open to roles in: AI/ML Engineering, Fullstack Development, Software Engineering, and Applied Research
- 📫 Reach out:
Built a full-stack job search management platform with AI-driven resume analysis (Llama 3.3 70B via Groq), ATS optimization, keyword gap detection, and a Chrome Extension to clip job listings directly from LinkedIn and Indeed.
- 💡 Built with FastAPI, React, PostgreSQL, and Llama 3.3 70B via Groq API
- 🔗 Includes a Chrome Extension (Manifest V3) for one-click job clipping from LinkedIn & Indeed
- 📊 AI analysis delivers match scores, missing keywords, ATS tips, and a rewritten resume summary
Built a high-performance URL shortening service with single-digit millisecond redirects, async click analytics, and geo/device parsing — engineered to handle massive traffic without blocking the Node.js event loop.
- 💡 Built with Node.js, Express, PostgreSQL, Redis, BullMQ, and React (Vite + Tailwind)
- ⚡ Decoupled redirect and analytics paths using a fire-and-forget BullMQ worker queue for non-blocking, high-volume click tracking.
- 🛠️ Conquered real-world engineering challenges, including cache data mismatches, BullMQ connection clashes, and aggressive browser caching.
Built an advanced Retrieval-Augmented Generation system for scientific PDFs — integrating section classification, citation graph analytics, figure detection, and local LLM generation (Mistral 7B).
- 💡 Used Grobid, LayoutLM, ChromaDB, and fine-tuned BERT
- 🔗 Full hybrid vector + keyword retrieval system with prompt optimization
- 📚 F1 Score: 99.54% (Reference Parsing), 79.94% (Section Classification)
AI diagnostic system combining X-ray imaging, clinical NLP, and tabular risk analytics into a unified medical assistant with prescription guidance.
- 🩻 DenseNet121 (CheXpert) + BioBERT + MLP Ensemble (Kidney, Heart, Diabetes)
- 🤖 Integrated with LLaMA for contextual recommendations
- 💻 Packaged with PyQt5 GUI and local data fusion
Production-grade PostgreSQL analytics engine with stored procedures, triggers, indexing, and planned LLM augmentation for feedback analysis.
- 🧠 11+ table schema, optimized queries, and stored functions in PL/pgSQL
- 📈 Real-time business metrics: customer behavior, delays, seller ranks
- 🧠 Extending with Zephyr LLM to auto-summarize trends & complaints
Custom YOLO variant optimized for occluded fruit detection in retail scenarios. Compared performance against SSD, Faster R-CNN.
- 🖼️ Trained on MinneApple + COCO with resolution-aware tuning
- 🚀 Real-time object detection via optimized bounding box anchors
Built a semantic image search engine that lets users query with text or images and returns top-matching results using precomputed embeddings.
- 🧠 Uses OpenAI CLIP + FAISS for vector similarity
- 🖼️ Visual & text-to-image search in real-time
Languages:
Frameworks & APIs:
AI & ML:
Data & Infra:
“Code is the closest thing we have to magic — I just make sure mine solves the right problems.”