Overview In this role you drive AI strategy and architecture for embedded and edge AI solutions. You partner with product teams and customers to define architectures, roadmaps, and reusable AI frameworks that deliver differentiated value. You will shape next-generation intelligent systems, advance edge intelligence, and mentor engineering teams. This role offers impact across multiple market segments and a chance to influence product differentiation through AI.
Compensation / Benefits- competitive benefits package
Responsibilities- Define AI/ML architectures across embedded, edge, cloud-connected systems, and sensor solutions
- Translate customer challenges and market trends into AI solution roadmaps
- Develop reusable AI frameworks, inference pipelines, and reference implementations
- Lead evaluation and adoption of ML/DL, foundation-model, and agentic-AI technologies
- Define architectures for sensor intelligence, signal processing, computer vision, anomaly detection, predictive analytics, sensor fusion, autonomous decision making, and multimodal AI
- Drive hardware/software partitioning across MCUs, MPUs, NPUs, DSPs, FPGAs, and cloud resources
- Define deployment strategies for AI workloads on resource-constrained embedded platforms
- Evaluate and optimize models for accuracy, latency, memory, power, and cost
- Collaborate with product, software, system, and research teams to integrate AI capabilities
- Support strategic customer engagements and act as a trusted AI architect advisor
- Monitor AI/ML advances and identify features for differentiation
- Contribute to AI reference architectures, papers, notes, patents, and ecosystem initiatives
- Provide mentoring and technical leadership to engineering teams
Key requirements- Master's or Ph.D. in a relevant field
- 10+ years in AI/ML, analytics, signal processing, computer vision, or intelligent embedded systems
- Proven leadership in AI architecture or equivalent roles
- Expertise in ML/DL, statistical modeling, signal processing, CV, sensor analytics, data fusion, and predictive analytics
- Strong experience with PyTorch, TensorFlow, ONNX, ML deployment frameworks
- Solid understanding of embedded systems, edge computing, heterogeneous compute architectures, MCUs/MPUs/NPUs, DSPs, AI accelerators, and optimization for performance, power, and memory
- Mentoring and technical leadership
- Collaborative cross-functional communication
- Strategic thinking and customer advisory capabilities
- PyTorch
- TensorFlow
- ONNX
