• Designed and implemented autonomous navigation algorithms using ROS, SLAM, and reinforcement learning, enabling mobile robots to

traverse highly unstructured terrains with ~92% accuracy, reducing human supervision needs in hazardous test environments.

• Developed deep learning computer vision models with TensorFlow, PyTorch, and OpenCV for obstacle avoidance and object recognition,

increasing robot perception accuracy by 30% and supporting real-time decision-making at scale.

• Engineered motion planning and trajectory optimization algorithms using Python, C++, and MoveIt, improving execution speed of robotic

arms by 20% in industrial pick-and-place applications, directly enhancing throughput.

• Built and deployed vision-based defect detection systems with YOLOv5 and OpenCV integrated into manufacturing workflows,

achieving 95% defect identification accuracy and cutting rework costs by 25% through proactive quality control.

• Leveraged AWS SageMaker and Lambda functions to deploy predictive maintenance ML models on robotic fleets, reducing unplanned

downtime by 18% annually and saving hundreds of labor hours.

• Designed real-time telemetry pipelines using Kafka, Airflow, and SQL to process robotic sensor data, reducing manual reconciliation

by 40% and ensuring high-integrity datasets for analytics and predictive modeling.

• Automated robotic test frameworks with Jenkins, Python, and ROS integration, shortening validation cycles by 35%, improving QA

efficiency, and accelerating release readiness.

• Partnered with hardware engineers on mechanical and control system optimization (SolidWorks, MATLAB/Simulink), enhancing robot

stability and energy efficiency, leading to smoother locomotion and lower power consumption across deployments.

• Worked cross-functionally with IoT, AI, and software teams to integrate real-time monitoring dashboards for robotics KPIs in Power BI and

Grafana, giving leadership visibility into system health and performance metrics.

Developed machine learning models (Python, TensorFlow, Scikit-Learn) for robotic arm fault prediction, boosting failure detection accuracy to 92% and cutting maintenance costs.

Created robotic simulation and digital twin environments (Gazebo, MATLAB/Simulink), reducing hardware testing costs by 28% and speeding up design cycles.

Built custom reinforcement learning algorithms to optimize robotic arm trajectories, improving assembly precision by 15%.

Engineered data pipelines (Airflow, Pandas, SQL) for sensor data, enhancing ML model reliability in production.

Integrated real-time computer vision systems (OpenCV, YOLOv4, PyTorch), increasing sorting speed and accuracy by 22%.

Designed low-latency control loops in ROS and C++ for sub-100ms feedback, doubling robot responsiveness.

Collaborated in Agile teams to streamline sprint planning, cutting release cycles by 20%.

Implemented safety-compliant AI models for Human-Robot Collaboration, aligning with OSHA and ISO standards.

Optimized GPU inference pipelines (CUDA, TensorRT), reducing latency by 40% for real-time vision tasks.

Contributed to documentation and knowledge sharing, standardizing ML practices and accelerating new engineer onboarding.