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.
- Define and implement scalable Edge AI deployment pipelines, monitoring frameworks, and lifecycle management processes.
- Contribute to technical solutioning, effort estimation, proposal development, and customer presentations.
- Engage directly with customers to define technical strategies, present solution architectures,



and defend proposed solutions during pursuits and pre-sales engagements.
- Mentor engineering teams on AI optimization techniques, deployment best practices, and emerging Edge AI technologies.

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.
- Robust knowledge of model compression, quantization, pruning, and acceleration techniques.
- Experience leveraging hardware accelerators such as GPU, NPU, DSP, TPU, and dedicated AI processing engines.
- Familiarity with AI frameworks including TensorFlow, PyTorch, ONNX, TensorRT, TFLite, or similar technologies.
- Experience with Edge AI deployment pipelines, MLOps practices, CI/CD integration, and model lifecycle management.
- Strong communication and stakeholder management skills with the ability to interact confidently with customers and business leaders.
- Bachelor's or Master's degree in Computer Science, Electronics, Artificial Intelligence, Data Science, or a related engineering discipline.

📌 EDge AI Architect (Bengaluru)
🏢 Capgemini
📍 Bengaluru