Navneet Dhamal

Developed AI/ML models using CNNs, YOLO v8, Faster R-CNN achieving 95%+ accuracy for production systems serving 2K+ users. Built real-time data processing pipelines using Python, FastAPI, and Apache Kafka, improving performance by 70% with <100ms latency. Architected scalable RESTful APIs using FastAPI and microservices, handling 500K+ daily requests with 99.9% uptime. Optimized deep learning models for production deployment, reducing inference time by 60% through quantization techniques. Technologies: Python, PyTorch, FastAPI, OpenCV, YOLO, Detectron2, Docker, PostgreSQL, Redis, AWS.

Designed computer vision algorithms for VFX production including object detection and semantic segmentation using deep learning. Built automated file management systems using Python and Shell scripting, reducing manual processing time by 80%. Developed ReactJS frontend components integrated with Django APIs, creating responsive interfaces for media processing applications. Implemented automated QA pipelines achieving 95%+ accuracy in content validation, reducing manual review time by 70%. Technologies: Python, Django, ReactJS, PyTorch, OpenCV, PostgreSQL, Docker, JavaScript.

Built full-stack web applications using Python FastAPI backend and ReactJS frontend for email marketing platform supporting 2K+ users. Developed robust REST APIs with authentication and validation, serving 50K+ daily requests with <200ms response time. Deployed AWS cloud infrastructure using EC2, S3, Lambda, RDS with auto-scaling, reducing infrastructure costs by 30%. Implemented CI/CD pipelines using GitHub, Jenkins and Docker, reducing deployment time from 4 hours to 15 minutes. Technologies: Python, FastAPI, Django, ReactJS, AWS, MySQL, PostgreSQL, Redis, Docker, Jenkins.