Advance your career as a Senior AI Platform & GPU-as-a-Service (GPUaaS) Engineer with NTT DATA. This pivotal role focuses on developing, managing, and optimizing our enterprise AI Platform & Acceleration Services (AIPAS). You will be instrumental in supporting cutting-edge workloads including Artificial Intelligence (AI), Generative AI (GenAI), Machine Learning (ML), Large Language Models (LLMs), and High Performance Computing (HPC), all leveraging GPU acceleration.

Collaborate closely with architecture, infrastructure, security, networking, storage, and business units to deliver a robust, production-ready GPU platform. Your work will directly enable scalable and secure AI adoption across the organization, transforming GPU infrastructure into a comprehensive enterprise GPU-as-a-Service offering.

Key responsibilities include overseeing platform engineering, facilitating customer onboarding, and enabling complex GPU workloads. You will ensure operational readiness, implement comprehensive observability strategies, manage capacity effectively, and drive automation to create self-service capabilities. Continuous service optimization is paramount. This role demands a blend of platform engineering acumen, hands-on operational experience, strong automation skills, and excellent customer engagement capabilities, enabling you to operate seamlessly across infrastructure, platforms, AI workloads, and operational domains.

We are seeking candidates with a Bachelor's degree in Computer Science, Engineering, Information Technology, or a related field, or equivalent practical experience. A minimum of 5 years in Platform Engineering, Infrastructure Engineering, Cloud Engineering, or Site Reliability Engineering (SRE) is required, alongside 3+ years of experience with Kubernetes and/or OpenShift. Proven experience operating enterprise production platforms, automating infrastructure services, and supporting highly available distributed systems is essential. Familiarity with Machine Learning and Large Language Models (inferencing and fine-tuning) is also key, as is experience working collaboratively across diverse technology and operations teams. Experience with NVIDIA GPU infrastructure is a must.