Machine Learning & Computer Vision Engineer
Building intelligent vision-based systems and exploring hybrid deep learning models at the intersection of research and real-world applications.
Passionate about AI, Computer Vision, and pushing the boundaries of innovation.
👋 Hi! I'm a Machine Learning and Computer Vision Engineer at Masterworks, where I develop intelligent vision-based systems and explore cutting-edge deep learning architectures.
🚀 My expertise spans from real-time object detection to generative modeling, bridging research and production-ready solutions. I specialize in Edge AI, MLOps and Generative Modeling
🎓 Education: B.Tech in Electronics and Communication Engineering from IIIT RGUKT RK Valley (2024)
📍 Location: Hyderabad, India
Python, C, C++, Rust
PyTorch, TensorFlow, YOLO, DeepStream SDK, NVIDIA TAO Toolkit
Docker, Kubernetes, MLflow, Weights & Biases, Dask, CI&CD
Machine Learning, Deep Learning, Geometric Deep Learning, Computer Vision, 3D Computer Vision, Generative Modeling, MLOps
Innovative solutions at the intersection of AI and Computer Vision, showcasing real-world impact.
Integrated multiple RTSP streams for real-time object detection and analytics. Optimized NVIDIA DeepStream pipelines with distributed training via PyTorch DDP across multiple GPUs, reducing training time by 35%. Utilized MLflow and Weights & Biases for experiment tracking, hyperparameter tuning, and performance visualization, ensuring scalable and reproducible MLOps workflows.
Built end-to-end detection and OCR pipeline using NVIDIA TAO Toolkit. Automated real-time number plate extraction from video streams.
Leveraged Grounding DINO for automatic annotation generation on unlabelled datasets, significantly reducing manual labeling effort.
Created synthetic datasets for license plate images and tabular time-series data using GANs. Enhanced model robustness through domain randomization, with distributed training via PyTorch DDP for faster generation. Leveraged MLflow and Weights & Biases for tracking experiments and optimizing hyperparameters, improving dataset quality by 30%.
Developed a hybrid model combining GANs and EBMs for controlled image generation. Implemented distributed training with PyTorch DDP to scale across GPUs, reducing training time by 40%. Used MLflow and Weights & Biases for experiment tracking and visualization, ensuring robust and reproducible training pipelines.
Deployed lightweight VLMs on edge devices for real-time action detection using RTSP video streams.
Developed statistical and Ensemble Models to predict off-block time probability (POBT) and vehicle inspection end times.
Built a defect detection pipeline using YOLO-based vision models for high-precision quality inspection.
Developed a novel generative model combining Mixture-of-Recursions with GANs to create high-quality synthetic images with enhanced diversity and realism. Utilized MLflow and Weights & Biases for comprehensive experiment tracking, hyperparameter optimization, and visualization of training metrics. Implemented distributed training via PyTorch DDP for efficient scaling across GPUs on both detection models (e.g., YOLO-based pipelines) and hybrid generative models, reducing training time by 40% while ensuring reproducibility in MLOps workflows.
Open to discussing ML research, computer vision projects, or collaboration opportunities.