Prathamesh Sardeshmukh

Machine Learning Engineer with 4+ years of experience in computer vision, real-time inference systems, and scalable ML pipelines. Proven expertise in designing production-grade AI systems involving distributed processing, GPU optimization, and edge deployment. Strong background in building high-throughput image and video processing pipelines using cloud and containerized infrastructure.

Machine Learning Engineer at Infinium Robotics (2023-02 – Present)

• Built and maintained scalable ML pipelines for real-time box counting, handling data ingestion, model training, inference, and deployment in production environments.
• Developed a Pallet Segmentation model using MaskRCNN with custom modifications, enabling precise geometric measurements; deployed optimized model into production.
• Improved box detection accuracy by implementing a Boundary Guided Network on top of RetinaNet and integrating custom loss functions from research.
• Designed continuous data pipelines for dataset versioning and retraining, improving model robustness and performance.
• Optimized inference pipelines for latency and throughput in real-world warehouse environments.

Machine Learning Engineer at Kabam Robotics (2021-11 – 2023-01)

• Architected a real-time video analysis pipeline enabling distributed processing of multiple ML models using asynchronous thread pool execution.
• Designed and deployed scalable streaming pipelines using AWS (KVS, KDS), Docker, and Kubernetes (EKS), handling high-throughput video ingestion and inference workloads.
• Containerized ML services and explored Kubernetes-based orchestration for scaling inference workloads across multiple services.
• Developed spill detection system using DeeplabV3 (0.67 mAP) through model ensembling under limited data constraints.
• Built people counting and tracking system using Kalman Filters and YOLO with custom logic for static object detection.
• Integrated ML pipelines with robot mission planner using MQTT protocol for real-time communication.

Research Engineer Intern at Tieset (2021-07 – 2021-10)

• Developed Deep Q Learning-based dynamic pricing model to optimize hotel revenue.
• Built reinforcement learning model for autonomous driving (speed and braking control) using CARLA and OpenAI Gym; deployed on NVIDIA Jetson.

Machine Learning Intern at Dataeaze Systems (2020-05 – 2021-06)

• Developed active learning-based human-in-the-loop data labeling pipeline, improving annotation efficiency.
• Built car damage detection system for insurance automation and deployed using Streamlit.
• Optimized neural networks using TensorRT (quantization + pruning) for edge deployment on Jetson Nano.
• Developed MaskRCNN-based asbestos fiber detection system with preprocessing optimizations using Otsu's method.

Bachelor's Of Technology in Instrumentation and Control Engineering – Vishwakarma Institute of Technology (2017 – 2021)