Job DescriptionGlobal Technology Head & Platform Architect – Multi-Agent AI & Computer Vision @Noida/Chennai/Bengaluru NNote: Below are the MUST HAVE's NProven record of buildinglarge scale AI platforms and taking them from concept to production ( — not limited to POCs, presales, or consulting) NR&D; leaders from product organizations (not consulting/implementation teams). NMust have taken AI platforms to production NRole: NNeed a globally experienced engineering leader, AI innovator, and platform architect to lead the evolution of an Physical AI Intelligence Platform. NHe/ She would serve as the Technology Champion and will be responsible for defining platform strategy, AI architecture, research direction, engineering execution, ecosystem partnerships, and industry leadership. NIdeal Candidate Profile N
- Core Expertiseno Strong background in Computer Vision / Video Intelligence — object tracking, segmentation, activity recognition. No Experience with multimodal AI (vision + other modalities). No Proven record of building large scale AI platforms and taking them from concept to production. No Hands on exposure to robotics perception stacks, industrial vision platforms, or autonomous driving vision systems. No Individuals who have architected or led AI platforms/products with explicit ownership of engineering and delivery.



NTechnical Must HavesnDeep familiarity with NVIDIA Metropolis and NVIDIA multi agency stacks. NUnderstanding of deployment environments (e.G., Kubernetes). NAbility to manage heterogeneous engineering teams (CV engineers, data scientists, simulation experts, embedded/cloud engineers). NMust have taken AI platforms to production — not limited to POCs, presales, or consulting. NMust have built reusable IP frameworks and accelerators during their journey. NNot looking for generic GenAI, RAG only, or pure cloud architecture profiles without production delivery. NQ&A; from the HM: NWhen assessing candidates, focus on: N1. Platform Ownership: What AI platform/product did they architect or lead? Was it from concept to production, or part of the journey? N2. Production Scale: At what scale was it deployed? What industries/customers? N3. Contribution Depth: Was their role individual contributor or organizational leader? N4. Engineering Org Size &Diversity;: How large and heterogeneous was the team they managed? N5. Challenges Faced: What were the key hurdles in taking the platform to production, and how did they overcome them? N6. Tech Stack: What level of expertise do they have with NVIDIA Metropolis, multi agency stacks, and deployment frameworks like Kubernetes?

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