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Experienced AI/ML Engineer Seeking Opportunities to Expand Expertise | Autonomous Driving | PyTorch | Nvidia GPU | CUDA | MLOps | AWS SageMaker | MLFlow | TensorRT | Docker | Jenkins | GitLab | Agile | Edge Deployments
I'm a seasoned machine learning engineer with extensive experience across the entire ML project lifecycle—from problem identification and solution development to production deployment and iteration. At Velodyne Lidar, I’ve developed AI models for complex tasks like object detection and segmentation, implementing critical MLOps practices. My expertise spans meticulous data preprocessing essential for model training, deploying models across resource-constrained embedded platforms, and optimizing performance in AWS cloud environments. I also established an Amazon S3 data repository for sensor data, streamlining preprocessing workflows. I’ve leveraged AWS SageMaker, MLflow, and Databricks for retraining models, ensuring pipeline robustness, integrity, and precise model version tracking, particularly with data drifts. My technical skill set includes Python, PyTorch, TensorFlow, AWS SageMaker, Databricks, and MLflow. During my tenure at Nvidia, I contributed to the AI City Challenge, focusing on data preprocessing for car object detection, ground truth annotations, and evaluation metrics. At Manthan Systems, I played a key role in developing and deploying the Merchandise Analytics retail product, managing feature changes, conducting comprehensive testing, and resolving frontend and backend issues. My achievements include IEEE paper publications on edge-based street object detection and smart parking solutions, winning 2nd place in the Cisco Network Intuitive hackathon, and contributing to cutting-edge projects at Nvidia, honing skills in hardware acceleration and computer vision algorithms. Proficient in a wide range of programming languages, data processing tools, and cloud platforms, I’m equipped to tackle complex ML and AI challenges. With a strong foundation in CI/CD practices and big data technologies, I strive to drive innovation and deliver impactful so
Westcliff University
Master of Business Administration - MBA, IT in Project Management
August 1, 2021 – May 1, 2023
San José State University
Master’s Degree, Embedded systems and AI specialization in Computer Engineering Department
January 1, 2016 – January 1, 2018
Dr.T.THIMMAIAH INSTITUTE OF TECHNOLOGY
Bachelor of Engineering (BE), Electrical, Electronics and Communications Engineering
January 1, 2009 – January 1, 2013
Ouster
Senior Machine Learning Engineer
July 1, 2018 – April 1, 2023
San Jose, California, United States · Hybrid
NVIDIA
Embedded and AI intern
February 1, 2018 – May 1, 2018
Santa Clara,CA · On-site
Algonomy
Software Engineer
January 1, 2014 – July 1, 2016
Bangalore · On-site
Edge-Based Street Object Detection
June 1, 2017 – August 1, 2017
- Participated in IEEE Smart World Nvidia AI City Challenge 2017. - Implemented a 15 classes DetectNet CNN model for Object Classfication. - Trained a YOLO model using DarkNet framework for Object Classification. - Analysed the performance of both DetectNet and YOLO model for the Nvidia Traffic Dataset.
Car Object Classification
March 1, 2017 – May 1, 2017
-Classified the car objects in the input image fed to the classifier with an accuracy of 79.28% using Tensor RT. -Worked on DIGITS framework to train a dataset and created a GoogleNet network based DNN model using caffe.
Custom device driver coding for ARM M3 microcontroller
January 1, 2017 – May 1, 2017
-Implemented GPIO, SPI, UART, I2C through embedded C/C++ and FREERTOS. -Experienced in reading and implementing code based on user manual for the controller.
Autonomous Spherical Robot
January 1, 2017 – May 1, 2017
-Kick started the robot locomotion from an Android app and avoided the obstacles by managing the FreeRTOS tasks of the Sensors and the DC motor using semaphores. -Logged the temperature sensor and acceleration sensor in the Android app and mapped the path traversed by the robot in the Android app using GPS tracking.
Cognitive Radio
August 1, 2016 – November 1, 2016
-Designed and developed a IEEE 802.11 protocol based system having ARM Cortex-M3 microcontrollers, RF modules to establish communication between two nodes. -Developed algorithms include LISA (Linear Invariant Synchronization Algorithm) for transmission and reception of data with synchronization, scrambling-descrambling and error check-correction.
Vehicle Supervisory Controller, MIO-BOSCH Ltd, Bangalore
March 1, 2013 – Present
Concentrated on designing and logic implementation of an Embedded system. The Vehicle Supervisory Controller using CAN protocol is a solution to increasing levels of pollution. It was designed to use a single PIC18F458 microcontroller. It controlled the two power sources simultaneously, which included the pump motor for controlling the fuel injection and electric motor to give power to the vehicle.Learnt about Embedded C language, CAN protocol, PIC18F458, hardware integration and designin
Learn by Doing - Prompt Engineering 101
KodeKloud
June 24, 2026 – Present
Introduction to OpenAI
KodeKloud
June 24, 2026 – Present
AWS Certified Solutions Architect – Associate
Amazon Web Services (AWS)
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity, ranging from embedded systems to advanced ML/AI, demonstrates a broad technical interest and adaptability. The experience at Ouster (formerly Velodyne Lidar) and NVIDIA aligns well with the target ML Engineer role, particularly in autonomous systems and computer vision. The blend of academic background (Embedded Systems and AI) and industry experience (Senior ML Engineer) suggests a strong foundation for a senior role. The MBA indicates an interest in broader business context, which can be beneficial for cultural fit in product-oriented teams.
Soft Skills & Operational Fit
The candidate's experience descriptions suggest an ability to manage customer requests, execute software releases, and work within Agile methodologies, indicating good operational fit. The project descriptions, while technical, are clear enough to infer a reasonable level of communication.