Key Responsibilities

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Define the end-to-end AI/Robotics architecture for intelligent and autonomous robotic systems.

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Design and develop robot perception pipelines for vision, sensor fusion, object detection, tracking, localization, and scene understanding.

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Develop and integrate Reinforcement Learning (RL) and Vision-Language-Action (VLA) based approaches for robotic decision-making, planning, and control.

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Design AI systems with Explainable AI (XAI) capabilities to improve transparency, interpretability, and debugging of robotic decisions.

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Develop uncertainty estimation and confidence-aware AI techniques for robust and safe operation in real-world environments.

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Architect and optimize Edge AI solutions for real-time inference on resource-constrained robotic platforms.

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Design cloud inference and production AI infrastructure for scalable model serving, monitoring, deployment, and lifecycle management.

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Develop parallel/distributed training pipelines for large-scale AI and robotics models using GPUs and multi-node compute infrastructure.

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Work closely with robotics, perception, controls, simulation, and software teams to translate research concepts into production-ready robotic systems.

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Evaluate emerging AI/robotics technologies and define technical roadmaps for next-generation robotic intelligence.

Core Technical Skills

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Solid experience in AI/ML system architecture and robotics AI.

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Strong understanding of robot perception and computer vision.

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Hands-on experience with Reinforcement Learning (RL) and modern learning-based robotics approaches.

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Understanding or practical experience with Vision-Language-Action (VLA), Vision-Language Models (VLMs), or multimodal AI.

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Knowledge of Explainable AI, uncertainty estimation, probabilistic modeling, and confidence-aware decision-making.

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Experience deploying AI models on edge/robotic hardware, with an understanding of latency, compute, memory, and power constraints.

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Experience building cloud-based inference and production ML systems.

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Strong understanding of parallel/distributed training, GPU computing, and large-scale model training.

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Strong programming skills in Python and C++.

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Experience with modern deep-learning frameworks such as PyTorch or TensorFlow.

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Experience with Qualcomm Platform, NVIDIA Jetson, CUDA, TensorRT.

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Experience with simulation platforms such as NVIDIA Isaac Sim, Gazebo, or similar.

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Experience with LLMs, VLMs, foundation models, multimodal models, and embodied AI.

Required Qualifications

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Bachelor’s or Master’s degree in Electronics, Mechtronics or related field

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5 - 10 years of experience in Product Management, Technical Product Management, Program Management, or a closely related role.

Job Location

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Bangalore

📌 Physical AI Architect (Bengaluru)
🏢 Cyient
📍 Bengaluru