EdgeAI Architect

JD
Choosing Capgemini means choosing a place where you'll be empowered to shape your career, supported by a collaborative global community, and inspired to reimagine what's possible. Join us in helping clients accelerate innovation through Edge AI, Embedded AI, and Intelligent Systems by bringing advanced machine learning capabilities to next-generation connected products and devices.
Your Role
As an Edge AI Solution Architect, you will lead the design, development, and deployment of AI/ML solutions on embedded and edge computing platforms. You will work closely with customers, product teams, and engineering organizations to architect scalable Edge AI solutions that leverage hardware accelerators while maximizing performance, power efficiency, and deployment scalability.
In this role, you will:

• Lead the architecture and implementation of Edge AI and Embedded AI solutions across a wide range of intelligent products and devices.
• Define end-to-end AI deployment strategies for embedded and edge computing environments.
• Architect AI/ML solutions on leading embedded platforms including NVIDIA Jetson, NXP i.MX, Qualcomm, and similar edge computing ecosystems.
• Collaborate with data scientists, software architects, and embedded engineering teams to transition AI models from development to production.
• Optimize machine learning and deep learning models for deployment on resource-constrained edge devices.
• Leverage hardware acceleration technologies including GPU, NPU, DSP, and AI accelerators to maximize inference performance and efficiency.

Your Profile

• 12+ years of experience in Embedded Systems, AI/ML Engineering, or Product Engineering.
• Proven expertise in AI and Edge AI model development, optimization, and deployment.
• Strong experience with embedded AI platforms such as NVIDIA Jetson, NXP i.MX, Qualcomm AI platforms, or equivalent ecosystems.
• Deep understanding of machine learning, deep learning, computer vision, and edge inference technologies.
• Hands-on experience optimizing AI models for constrained embedded devices and microcontroller-based systems.
• Strong knowledge of model compression, quantization, pruning, and acceleration techniques.
• Experience leveraging hardware accelerators such as GPU, NPU, DSP, TPU, and dedicated AI processing engines.