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