Physical AI Architect

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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Strong 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

Roles and Responsibilities
Expert within chosen area of work.
Analyze & create Solutions to Complex technical problems.
Performs Quality checks for complex tasks and internal Technical reviews.
Drive Technical discussions with customer SMEs and support product reviews.
Work with SLDMs in identification and development of mistake proofing solutions to improve quality.
Support Go to Market teams on POC’s and Bids.
Participate in Technical feasibility studies for identified automation proposals.
Competency management & Mentoring associates in collaboration with discipline managers.
Development of standard work & training content.
Publish whitepapers and write Blogs.
Typically requires a Bachelor's degree in Engineering and relevant experience in electronics & computer design domain.