Vision Systems Architect _ Dubai, United Arab Emirates (UAE Fly-In Model)

Project description • Owns the end-to-end vision and AI architecture for consumer devices. Defines scalable, power-efficient, and production-ready imaging and perception systems across product lines. Drives technical direction across sensor, ISP, CV, ML, and platform integration. Responsibilities • Define and own end-to-end vision system architecture (sensor → ISP → CV/ML → application layer). • Translate product and UX requirements into scalable technical solutions. • Define image quality strategy aligned with AI and user experience goals. • Architect reusable, modular CV/ML frameworks across multiple product SKUs. • Drive system-level trade-offs across performance, power, cost, and thermal limits. • Define AI model lifecycle strategy from training to OTA deployment. • Architect heterogeneous compute utilization (CPU/GPU/NPU/DSP). • Define system KPIs: latency, FPS, power, memory, boot time, and robustness. • Lead technology selection for inference engines, frameworks, and hardware platforms. • Guide SoC bring-up and performance validation for vision pipelines. • Establish coding standards, modularization strategy, and long-term maintainability. • Identify technical risks early and define mitigation strategies. • Collaborate with hardware, ISP, Android/Linux platform, and product teams. • Mentor senior engineers and provide architectural governance. • Contribute to long-term vision/AI roadmap across product generations. Skills Must have • Bachelor's or Master's degree in Computer Science, Electrical Engineering, or related field (PhD preferred). • 10-15+ years of experience in imaging/vision/perception systems. • Proven experience architecting and shipping multiple consumer products with vision/AI. • Deep understanding of imaging pipeline (sensor behavior, ISP, tuning impact). • Strong embedded system architecture experience. • Expert-level C++ and system-level optimization knowledge. • Strong expertise in CV and ML system integration. • Experience deploying optimized ML models on consumer SoCs. • Experience with heterogeneous compute architectures. • Experience defining system-level KPIs and validation strategies. • Strong cross-functional leadership and communication skills. Nice to have • Experience with computational photography pipelines. • Experience in AR/VR or advanced camera features. • Experience with silicon bring-up and HW/SW co-design. • Experience with large-scale model deployment and OTA updates. • Experience in multi-camera or depth-sensing systems. • Contributions to patents or technical publications. • Experience leading architecture across multiple product generations. Languages English: B2 Upper Intermediate Seniority Senior Dubai, United Arab Emirates Req. VR-121112 Show more Show less

Bachelor's or Master's degree in Computer Science, Electrical Engineering, or related field (PhD preferred). 10-15+ years of experience in imaging/vision/perception systems. Proven experience architecting and shipping multiple consumer products with vision/AI. Deep understanding of imaging pipeline (sensor behavior, ISP, tuning impact). Strong embedded system architecture experience. Expert-level C++ and system-level optimization knowledge. Strong expertise in CV and ML system integration. Experience deploying optimized ML models on consumer SoCs. Experience with heterogeneous compute architectures. Experience defining system-level KPIs and validation strategies. Strong cross-functional leadership and communication skills.

Define and own end-to-end vision system architecture (sensor → ISP → CV/ML → application layer). Translate product and UX requirements into scalable technical solutions. Define image quality strategy aligned with AI and user experience goals. Architect reusable, modular CV/ML frameworks across multiple product SKUs. Drive system-level trade-offs across performance, power, cost, and thermal limits. Define AI model lifecycle strategy from training to OTA deployment. Architect heterogeneous compute utilization (CPU/GPU/NPU/DSP). Define system KPIs: latency, FPS, power, memory, boot time, and robustness. Lead technology selection for inference engines, frameworks, and hardware platforms. Guide SoC bring-up and performance validation for vision pipelines. Establish coding standards, modularization strategy, and long-term maintainability. Identify technical risks early and define mitigation strategies. Collaborate with hardware, ISP, Android/Linux platform, and product teams. Mentor senior engineers and provide architectural governance. Contribute to long-term vision/AI roadmap across product generations.