Lead GenAI & Agentic AI platform strategy for multiple clients at Capgemini Engineering. Driving adoption, managing operations, and advising on technological architecture.
About the role
- ROS Architect optimizing ROS architectures and software for Physical AI systems at ST Engineering. Engaging in cutting-edge robotics applications and collaborative hardware engineering efforts.
Responsibilities
- Define and implement robust ROS architectures including workspace structure, package management, and node communication paradigms.
- Configure ROS QoS policies and security features, runtime management (Executor), and modify/add client libraries.
- Develop, maintain, and containerize (Docker) complex launch configurations and parameter management systems.
- Design, code, test, and debug software nodes, services, and actions in C++ or Python to control Physical AI systems.
- Customize Linux kernel performance, scheduling, and resource allocation to ensure low-latency performance for robotics applications and other safety-critical tasks.
- Identify and resolve low-level system failures, debugging race conditions, and handling memory management to improve stability.
- Design and manage the communication framework (e.g., DDS, MQTT) that allows nodes, sensors, and actuators to exchange data in real-time.
- Write and optimize device drivers for sensors (Lidar, cameras, IMUs) and actuators (motors, servo controllers) to work with Linux and ROS.
- Work closely with hardware engineers, application teams, and QA testers, and create technical documentation for system architecture.
Requirements
- Masters/PhD in Computer Science, Machine Learning, AI, Robotics, or related field.
- Masters (10 – 15 years)/ PhD (3-5 years) of experience building robotics/Physical AI systems.
- Strong understanding of operating system concepts, including process management, multi-threading, concurrency, memory management (paging/virtual memory), and interrupt handling.
- Expert knowledge of Ubuntu Linux, terminal usage, shell scripting (Bash), and networking (TCP/IP).
- Deep knowledge of ROS1 & ROS 2, especially ROS 2 and its middleware components (DDS, FastDDS/CycloneDDS).
- Experience with Behavior Trees and other robot behavior orchestration frameworks.
- Deep proficiency in C and C++ is essential for kernel-level development, with Rust increasingly required for modern systems, and Assembly for debugging and performance tuning.
Benefits
- Opportunity to work on state-of-the-art embodied AI models powering real robots.
- Combination of research and deployment — not just writing models, but seeing them act in the physical world.
- High-impact work on cutting-edge robotic autonomy and swarm behaviours.
- Exposure to both AI and hardware-level execution environments.
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