Senior Full Stack & AI Engineer • AI Research Assistant (CV)
M.S. Artificial Intelligence, specialized in Computer Vision @ UCF • Ex-Infoscian • Full Stack Developer
Open to Full Stack / AI-Integrated Full Stack / Computer Vision Engineering roles
I'm a Senior Full Stack Engineer with 10+ years of experience designing, building, and running enterprise Java/Python applications end-to-end — from backend services and REST/SOAP APIs to responsive React/Angular front ends and cloud deployment.
Over the last year, I've extended that full-stack foundation into AI, GenAI, and Computer Vision, completing an M.S. in Artificial Intelligence (Computer Vision) at UCF and shipping production-style applications that combine traditional full-stack engineering with:
- Computer Vision: object detection, pose estimation, tracking, and video analytics (YOLOv8, MediaPipe, OpenCV)
- GenAI / LLMs / Agentic AI: RAG pipelines, multi-agent orchestration, tool-using agents, prompt design and evaluation (LangChain, LangGraph)
- ML Integration into Full-Stack Apps: taking a trained model and wiring it into a real backend, API layer, and UI — not just a notebook
- Production Engineering: Spring Boot/FastAPI services, REST APIs, CI/CD, Docker/Kubernetes, AWS/GCP, monitoring and reliability
I like building the whole thing — the model, the API around it, and the interface a real user touches.
Senior Full Stack Developer & AI Integration — COMPETE BePlayFuel — Feb 2026 – Present
Built a full-stack, AI-powered video-processing application: integrated a YOLOv8 pose-estimation + MLP classification pipeline into the backend, translating model output into real-time scoring and user-facing feedback across REST APIs and a React.js UI. Deep Learning · Machine Learning · Python · Java · React.js · OpenCV · PyTorch · YOLOv8 · Flask/FastAPI · GCP · Docker
University of Central Florida — Part-time — 10 mos
- Graduate Teaching Assistant — Jan 2026 – May 2026
- Graduate Research Assistant — Aug 2025 – Jan 2026
Engineered scalable batch-processing and data pipelines for an Urban Pedestrian Detection, Tracking & Behavior Analysis platform — 47M+ structured detection records across 286 georeferenced scenes.
Deep Learning · Machine Learning · Python · SQL · OpenCV · GCP/HPC
Machine Learning Engineer (Full Stack AI) — N2 Services Inc — Remote — May 2025 – Aug 2025
Built a cloud-deployed, AI-powered Multimodal Visual Recognition System: integrated a MediaPipe pose-estimation pipeline with an MLP classifier into a Python backend, delivering real-time scoring at 30 FPS through a responsive UI. Deep Learning · Machine Learning · Python · Flask/FastAPI · MediaPipe · Streamlit · GCP
Technology Analyst (Java Full Stack Developer) — Infosys — Chennai, Tamil Nadu, India · Hybrid — Mar 2022 – Jul 2024
Designed and built enterprise application components Java, J2EE, Spring Boot, REST/SOAP APIs, plus CI/CD pipelines and AWS migration support. Databases · Git · Java · J2EE · Spring Boot · AWS
Java Developer — N2 Software Services Private Limited — Greater Chennai Area · Hybrid — Aug 2014 – Feb 2022
Delivered scalable Java/J2EE applications across the full SDLC using Spring, Hibernate, and microservice patterns. Databases · Git
Full project details and metrics are in my résumé — happy to walk through any of these.
- Full-stack applications with an AI/ML model wired into the backend, not bolted on after
- Real-time computer vision pipelines (detection, tracking, pose estimation)
- Agentic AI workflows and tool-using assistants
- Retrieval-Augmented Generation (RAG) systems
- Medical imaging and diagnostic support platforms
- Scalable REST/microservice backends deployed on AWS/GCP with CI/CD
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Problem: Give users real-time feedback on movement quality from video. Impact: Full-stack app (React.js + Flask/FastAPI) with a YOLOv8 + MLP pipeline in the backend, producing scored, annotated video feedback in real time. |
Problem: Real-time pose recognition and correctness scoring at 30 FPS. Impact: MediaPipe + MLP classifier integrated into a Python backend, deployed on GCP with session analytics and live feedback. |
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Problem: Automate desktop form-filling transparently. Impact: Computer-use agent turning UI perception into structured JSON plans, executing clicks/typing via a perception–reasoning–action loop. |
Problem: Detect and track pedestrians across intersections. Impact: YOLOv8 + DeepSORT pipeline across hundreds of scenes with trajectory-based analytics. |
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Problem: Measure and explain gender bias in multimodal reasoning models. Impact: LoRA fine-tuned VLM + Chain-of-Thought pipeline classifying bias with human-readable rationales. |
Problem: Turn family memories into personalized children's storybooks. Impact: Multi-agent platform (Safety, Narrative, FactCheck, Pedagogy, Quiz, Visual agents) producing safe, illustrated stories. |
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Problem: Classify multimodal messages into notify / digest / mute. Impact: Agent router combining OCR, Whisper transcription, hybrid retrieval, and confidence calibration. |
Problem: Unified, interpretable medical imaging pipeline for multiple diseases. Impact: Diagnosis across five conditions with Grad-CAM heatmaps explaining predictions to clinicians. |
Languages: Java, Python, JavaScript/TypeScript, SQL Full Stack: Spring Boot, Spring MVC, Hibernate, REST/SOAP, React.js, AngularJS, Flask, FastAPI AI / ML: Deep Learning, Machine Learning, GPT-4, Gemini, LangChain, LangGraph, RAG, Vector Search (FAISS, Pinecone) Computer Vision: YOLOv8, MediaPipe, OpenCV, PyTorch Cloud / DevOps: AWS, GCP, Docker, Kubernetes, Jenkins, CI/CD, Git Databases: Oracle, MySQL, PostgreSQL, MongoDB
Full stack at the core. AI at the edge. Building systems that see, reason, and act.