Product Manager

Job Title: Product Manager-AI Platform Architect Location: Noida Experience: 10-16 Years

The Role The AI Platform Architect designs and governs centralized platforms that enable scalable, reusable, and secure AI development and operations across the enterprise. This role focuses on platform capabilities rather than individual solutions. Competency Focus:

AI platform engineering, MLOps systems, cloud architecture, enterprise enablement

Responsibilities:

Architect and define enterprise‑scale, GPU‑accelerated AI platform reference architectures leveraging NVIDIA technologies, Kubernetes, and cloud‑native patterns to support high‑performance, scalable, and resilient AI, ML, and GenAI workloads aligned with strategic business use cases. Establish and govern cloud‑native AI platform standards using Kubernetes and container orchestration best practices, enabling consistent and secure deployment of NVIDIA GPU‑enabled AI workloads across private, public, and hybrid cloud environments, with a strong focus on scalability, reliability, and performance optimization. Define and institutionalize AI model lifecycle management best practices, including model versioning, validation, governance, controlled deployment, observability, and continuous monitoring on GPU‑backed AI platforms to ensure operational stability and auditability. Design and enforce containerization and deployment standards using Docker and Kubernetes for AI workloads, ensuring portability, reproducibility, efficient GPU utilization, and seamless CI/CD and MLOps integration across environments. Partner with Information Security, AI Legal, and Regulatory teams to define and enforce enterprise‑wide security and compliance standards for AI platforms, covering GPU workload isolation, identity and access management, data encryption, audit logging, and regulatory adherence. Monitor and analyze GPU, compute, memory, and storage utilization across AI platforms, recommend cost‑optimization and capacity‑planning strategies, and drive efficiency improvements for AI workloads in cloud and on‑prem environments. Provide regular platform health, performance, and cost reports to key stakeholders. Continuously evaluate and improve AI system performance, optimizing GPU utilization, inference latency, training throughput, and platform stability to ensure AI services deliver maximum efficiency, reliability, and business value. Serve as a key technical advisor to the AI CoE Lead and senior leadership, contributing to the definition of the organization’s AI platform vision and ensuring alignment between NVIDIA‑based AI infrastructure strategy and broader business goals. Collaborate with business and technical stakeholders to identify new AI platform opportunities, uncover high‑value use cases, and design innovative GPU‑accelerated AI solutions that improve operational efficiency, enhance customer experience, and support strategic objectives. Identify, assess, and mitigate architectural, operational, and performance risks associated with deploying and operating AI models on Kubernetes‑based, GPU‑enabled platforms, ensuring reliability and scalability at enterprise scale. Work closely with data scientists, MLOps engineers, network engineers, and application development teams to enable seamless integration of AI models, data pipelines, and GPU‑accelerated services into enterprise applications and workflows.

Qualifications & Experience

B. Tech/B.E. in Computer Science, Engineering, or Information Technology; M.Tech/MS preferred Minimum 9–14 years of overall IT or platform engineering experience 5–7 years of experience designing enterprise‑scale AI or data platforms Proficiency in AI and machine learning frameworks (TensorFlow, PyTorch, Keras) Proficient with public cloud platforms (AWS, Azure, Google Cloud) Experience in containerization and orchestration tools (e.g.: Docker, Kubernetes) Certifications Required: NVIDIA Certified Professional: AI Infrastructure and Operations NVIDIA Certified Professional: AI Networking Certified Kubernetes Administrator Cloud Certification (AWS, Azure, GCP)