AI Engineering Manager— Vision AI & Edge Intelligence

Role Summary We are seeking an experienced AI Engineering Manager to lead the development of Vision AI and Edge Intelligence systems for real-time, low-latency applications. The role focuses on building end-to-end AI systems across perception models, multimodal intelligence, real-time inference optimization, and edge deployment. This position emphasizes production-grade delivery, system performance, scalability, and real-world deployment quality . Key Responsibilities Vision AI Development & Edge Real-Time Inference • Lead end-to-end Vision AI development, including object detection, segmentation, tracking, video understanding, and semantic scene understanding • Drive the adoption of multimodal and vision-language models, including MLLMs, VLMs, and Vision Agent architectures for natural-language interaction and agentic perception workflows • Design and optimize low-latency inference pipelines for edge deployment, balancing model accuracy, latency, compute efficiency, memory usage, and deployment feasibility • Apply model optimization techniques such as quantization, pruning, knowledge distillation, TensorRT, ONNX, or similar production inference frameworks • Ensure real-time system performance for production applications Cross-Functional Integration • Work closely with platform, system, and RAN teams to integrate AI capabilities into commercial products • Translate product requirements into robust AI system designs and implementation plans • Ensure AI solutions meet real-world deployment constraints, including latency, compute, reliability, and maintainability Team Leadership • Lead, mentor, and grow a high-performing AI engineering team • Define the technical roadmap for Vision AI and Edge Intelligence capabilities • Evaluate, adopt, and operationalize emerging AI technologies and system architectures Required Skills & Experience • Strong background in computer vision, deep learning, and production AI system development • Proficiency in PyTorch, TensorFlow, or equivalent deep learning frameworks • Hands-on experience with detection, segmentation, tracking, video analytics, or related vision AI applications • Practical experience with model deployment and optimization using ONNX, TensorRT, or similar tools • Proven ability to build and scale AI systems from prototype to production Preferred Skills • Experience with multimodal learning, vision-language models, foundation model adaptation, MLLMs, VLMs, or related multimodal AI systems • Knowledge of Vision Agent concepts, including vision-language reasoning, video question answering, video summarization, visual grounding, and agentic interaction with live or recorded video streams • Knowledge of distributed inference systems and cloud-edge collaborative architectures • Experience with Kubernetes, containerized deployment, or cloud-edge infrastructure • Background in real-time video processing, telecom systems, robotics, or Physical AI applications Education & Qualifications • Bachelor’s degree or higher in Computer Science, Artificial Intelligence, Electrical Engineering, or related technical field • Master’s or PhD preferred for senior candidates or candidates with strong research background • Strong foundation in machine learning, deep learning, or applied mathematics is highly desirable Experience Requirements • Minimum 8 years of relevant industry experience in AI / Machine Learning / Computer Vision • Expert in Nvidia Metropolis, experience in Nvidia Isaac, Cosmos and Omniverse desired • Proven track record of delivering production-grade AI systems in real-world environments • Experience in edge AI, real-time systems, or large-scale deployment is highly preferred