You will
- Design and develop AI-powered systems that interact with the physical world through robots, sensors, cameras, and connected devices.
- Collaborate with robotics, software, hardware, and product engineering teams to build intelligent, autonomous, and real-time AI solutions.
- Develop scalable AI models and autonomous systems that leverage computer vision, sensor fusion, machine learning, and edge computing to solve real-world challenges.
Responsibilities
- Design, develop, and deploy AI models for robotics and real-world physical systems.
- Build perception systems using computer vision, deep learning, and sensor fusion techniques.
- Develop autonomous decision-making, navigation, path planning, and motion control algorithms.
- Integrate cameras, LiDAR, IMUs, GPS, and other sensors into AI-driven systems.
- Develop and validate AI models using simulation environments and digital twins.
- Implement machine learning, deep learning, and reinforcement learning solutions for autonomous operations.
- Optimize AI models for edge devices and real-time deployment.
- Collaborate with cross-functional teams across robotics, software, hardware, and product engineering.
- Evaluate system performance and improve reliability, scalability, safety, and operational efficiency.
- Participate in architecture reviews, testing, debugging, troubleshooting, and production deployments.
- Create and maintain technical documentation, deployment procedures, and engineering best practices.
Qualifications
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Robotics, Electronics, Mechatronics, or a related discipline.
- 3–6 years of experience in AI, Machine Learning, Robotics, Autonomous Systems, or related domains.
- Strong programming skills in Python and at least one of C++ or Java.
- Hands-on experience with machine learning and deep learning frameworks such as PyTorch, TensorFlow, and Keras.
- Strong experience in computer vision techniques including Object Detection, Image Classification, Instance Segmentation, Pose Estimation, and Visual SLAM.
- Experience working with robotics frameworks such as ROS/ROS2, Gazebo, and Isaac Sim.
- Strong understanding of sensor integration, sensor fusion, autonomous navigation, path planning, and motion control.
- Experience developing AI solutions for edge devices and familiarity with Linux-based development environments.
- Knowledge of Git, Agile development methodologies, and software engineering best practices.
- Experience with Generative AI for robotics, Reinforcement Learning, Multi-Agent Systems, NVIDIA Jetson, Isaac Platform, CUDA, GPU acceleration, and model optimization is preferred.
- Exposure to cloud platforms such as Azure, AWS, or Google Cloud, along with MLOps, CI/CD pipelines, and model lifecycle management is desirable.
- Experience in Industrial Robotics, Autonomous Mobile Robots (AMR), Warehouse Automation, Autonomous Vehicles, Smart Manufacturing, Drones/UAVs, Smart Cities, IoT, or Healthcare Robotics is an advantage.
- Excellent analytical, problem-solving, communication, collaboration, and troubleshooting skills with a strong ownership mindset and passion for AI and robotics technologies.