AI/ML Engineer - Robotics and Industrial Automation

Role Summary

As an AI/ML Engineer, you will be responsible for designing, training, and deploying machine learning models that solve complex challenges in robotics and industrial automation. You will work on computer vision, reinforcement learning, path planning, SLAM, and sensor fusion, ensuring seamless integration into robotic platforms. This role requires strong expertise in deep learning, model optimization, and real-time AI deployment on edge devices.

Key Responsibilities

1. AI/ML Model Development

• Develop Machine Learning Models: Design and train ML models for object detection, path planning, SLAM (Simultaneous Localization and Mapping), and sensor fusion.
• Apply Deep Learning Techniques: Utilize TensorFlow, PyTorch, and advanced neural networks for computer vision, navigation, and decision-making tasks.
• Implement Reinforcement Learning: Develop RL models for optimizing robotic movement, task execution, and real-time decision-making in dynamic environments.

2. Model Optimization & Deployment

• Optimize for Edge Devices: Apply quantization, pruning, and knowledge distillation to deploy AI models efficiently on NVIDIA Jetson, ARM Cortex, and other embedded platforms.
• Deploy AI in Production: Work with Edge AI and Embedded Systems teams to integrate AI models into robotic control systems, ensuring low-latency, high-accuracy performance.
• Monitor & Improve Models: Implement feedback loops and performance monitoring tools to track and enhance deployed models based on real-time data.

3. Data Preparation & Processing

• Collaborate on Data Pipelines: Work with data engineers to preprocess and clean large-scale datasets collected from IoT devices, sensors, and industrial robots.
• Feature Engineering: Extract and engineer relevant features to improve model performance and ensure generalization across different environments.
• Augment Data for AI Training: Implement data augmentation and synthetic data generation techniques to improve model robustness and reliability.

4. Integration with Robotic Systems

• Collaborate with Cross-Functional Teams: Work with robotics engineers, AI/ML experts, and software developers to integrate AI models into robotic platforms.
• Ensure ROS Compatibility: Develop AI models that work seamlessly with Robot Operating System (ROS) and enable communication between AI modules and robotic control stacks.
• Optimize for Real-Time Execution: Ensure AI models can handle real-time inference in complex industrial scenarios with minimal latency.

5. Research & Innovation

• Explore Emerging AI Techniques: Stay up to date with advancements in generative AI, graph neural networks, multi-agent learning, and neuromorphic computing.
• Develop Proof-of-Concepts: Prototype innovative AI solutions for robotics and industrial automation.
• Contribute to Open-Source Projects: Participate in AI/ML open-source communities to share innovations and improve industry standards.

6. Documentation & Compliance

• Document AI/ML Development Processes: Maintain detailed documentation of model architectures, training methodologies, and deployment workflows.
• Adhere to Safety & Compliance Standards: Ensure AI models meet industry safety regulations, ethical AI guidelines, and industrial compliance standards.

Required Qualifications - Education & Experience

• Bachelors or Masters degree in Computer Science, Machine Learning, Robotics, or a related field.
• 3+ years of hands-on experience in AI/ML development, preferably in robotics, industrial automation, or autonomous systems.
• Proven track record of developing, optimizing, and deploying AI/ML models for real-world applications.

Technical Skills

• Machine Learning & AI:
• Expertise in TensorFlow, PyTorch, Scikit-Learn, and other ML frameworks.
• Strong understanding of deep learning, reinforcement learning, and probabilistic models.
• Programming & Development:
• Advanced skills in Python and familiarity with C++ (for robotics applications).
• Experience with data manipulation libraries (Pandas, NumPy, SciPy).
• Edge AI & Embedded Systems:
• Experience deploying AI models on embedded hardware like NVIDIA Jetson, STM32, ARM Cortex.
• Proficiency in model optimization using TensorRT, OpenVINO, and ONNX.
• Robotics & IoT Data Processing:
• Familiarity with robotics control systems, SLAM, and IoT sensor data.
• Experience integrating AI models with ROS-based robotic platforms.

Preferred Qualifications

• Robotics & ROS Expertise: Experience working with Robot Operating System (ROS) and robotic perception pipelines.
• Optimization for Edge AI: Hands-on experience optimizing AI inference for edge devices with low-latency execution.
• Project Management & Agile: Familiarity with Agile development, JIRA, and collaborative DevOps workflows