Technology Platform Engineer

Accenture PLC; Bengaluru; On-site; INR 3,500,000 - 5,500,000; Full time

Job summary

Accenture PLC is seeking a Technology Platform Engineer to design and deploy HPC and AI infrastructure across on-premises, cloud, and hybrid environments. You will build scalable clusters and manage XPU-based systems.

You will optimize performance, ensure security, and collaborate with analysts and engineers to run large-scale models and simulations. Strong Linux and CUDA GPU expertise required; cloud and DevOps skills preferred.

Qualifications

• Experience in enterprise-wide HPC strategy and architecting next-gen supercomputing environments across the full stack.
• Primary Skills: Linux Administrator/Architect, Advanced CUDA/GPU on H100/A100, HPC cluster design (SLURM/PBS Pro)
• Secondary Skills: Parallel programming: MPI, OpenMP, CUDA, SYCL, NVIDIA DGX SuperPOD & BCM, InfiniBand NDR 400Gb/s & RoCEv2 design, MLOps & HPC integration (Kubeflow), Containerization: Singularity, Kubernetes, DevOps: Ansible, Terraform for HPC, UFM management, RunAI, Azure ML integration: distributed training, MLflow, Terraform / Bicep IaC for Azure HPC, FinOps: Reserved Instances, Spot VM strategies, Hybrid cloud HPC: on-prem to Azure/AWS burst

Responsibilities

• Design and implement HPC and AI infrastructure solutions, aligning system architecture and deployment roadmaps to industry-specific performance and scalability needs
• Deploy, configure, and manage XPU-based clusters (CPU/GPU/accelerators) using schedulers, VM/K8s orchestration platforms, Slurm, and containerized platforms in scalable designs to provide MaaS, GPUaaS, AIaaS, and other offerings
• Optimize cluster performance, scalability, energy, and cost efficiency across on-premises, cloud, and hybrid environments
• Integrate AI and HPC platforms with existing IT systems, data pipelines, and security frameworks
• Monitor, troubleshoot, and tune infrastructure to ensure high availability, low-latency networking, and workload resiliency
• Develop and maintain documentation including architecture diagrams, configuration baselines, and operational runbooks
• Provide technical guidance and support to users, enabling efficient execution of HPC/AI workloads, large-scale models, and simulations.

Skills

Linux Administrator/Architect

CUDA/GPU

HPC cluster design

Education

15 years full time education

Tools

SLURM

PBS Pro

Kubernetes

Docker

Terraform

Ansible

Run:AI

Job description

Project Role : Technology Platform Engineer

Project Role Description : Creates production and non-production cloud environments using the proper software tools such as a platform for a project or product. Deploys the automation pipeline and automates environment creation and configuration.

Must have skills : Linux Architecture

Good to have skills : Machine Learning (ML), Cloud Technology Architecture, Docker Kubernetes Administration, Edge Computing, Microsoft Agentic AI Architect

Minimum 12 year(s) of experience is required

Educational Qualification : 15 years full time education

Key Responsibilities

• 1) Design and implement HPC and AI infrastructure solutions, aligning system architecture and deployment roadmaps to industry-specific performance and scalability needs
• 2) Deploy, configure, and manage XPU-based clusters (CPU/GPU/accelerators) using schedulers, VM/K8s orchestration platforms, Slurm, and containerized platforms in scalable designs to provide Metal as a Service (MaaS), GPUaaS, AIaaS, and other offerings
• 3) Optimize cluster performance, scalability, energy, and cost efficiency across on-premises, cloud, and hybrid environments
• 4) Integrate AI and HPC platforms with existing IT systems, data pipelines, and security frameworks
• 5) Monitor, troubleshoot, and tune infrastructure to ensure high availability, low-latency networking, and workload resiliency
• 6) Develop and maintain documentation including architecture diagrams, configuration baselines, and operational runbooks
• 7) Provide technical guidance and support to users, enabling efficient execution of HPC/AI workloads, large-scale models, and simulations.

Required Skills and Qualifications

• 1) Experience in enterprise-wide HPC strategy and architecting next-gen supercomputing environments across the full stack.
• 2) Primary Skills: Linux Administrator/Architect, Advanced CUDA/GPU on H100/A100, HPC cluster design (SLURM/PBS Pro)
• 3) Secondary Skills: Parallel programming: MPI, OpenMP, CUDA, SYCL, NVIDIA DGX SuperPOD & BCM, InfiniBand NDR 400Gb/s & RoCEv2 design, MLOps & HPC integration (Kubeflow), Containerization: Singularity, Kubernetes, DevOps: Ansible, Terraform for HPC, UFM management, RunAI, Azure ML integration: distributed training, MLflow, Terraform / Bicep IaC for Azure HPC, FinOps: Reserved Instances, Spot VM strategies, Hybrid cloud HPC: on-prem to Azure/AWS burst
• 4) Proven hands-on experience designing, deploying, and managing HPC and AI infrastructure across on-premises, cloud, and hybrid environments in 2 or more segments: hyperscaler, neocloud, large Enterprise, Telco/Mobile, supporting key industries such as Financial Services, Life Sciences, Manufacturing, and Retail
• 5) Deep knowledge of accelerated computing architectures (GPUs, XPUs, DPUs), high-performance fabrics (InfiniBand, Ethernet), SONiC, networking, and modern storage/data platforms (e.g. NVMe-oF, Lustre, GPFS, BeeGFS, VAST, DDN, Weka) to build robust solutions
• 6) Proficiency with cluster management and orchestration (e.g. Slurm, Run:ai, Kubernetes, Docker), real-time performance monitoring, and observability frameworks
• 7) Hands-on experience with cloud and virtualization platforms (e.g. AWS, Azure, GCP, VMware, Nutanix) and expertise in automation and optimization using scripting (Python, AI tools) with foundational Infrastructure-as-Code tools such as Terraform and Ansible.

Preferred Skills and Qualifications

• 1) Experience managing the deployment of 1,000+ GPU clusters for HPC and AI workloads with various infrastructure services enabled
• 2) Experience with GPU computing libraries and accelerators (e.g., NVIDIA CUDA, Dynamo, AMD ROCm).
• 3) Experience with AI and HPC Networking (e.g., RoCE, InfiniBand, muti-planar/multi-rail designs, platform buffer architectures)
• 4) Knowledge of Machine Learning and AI frameworks (e.g., TensorFlow, PyTorch, JAX), Jupyter notebooks / Google Colab environments
• 5) Familiarity with DevOps practices and tools (e.g., Ansible, Terraform) for infrastructure automation
• 6) Experience with AgenticAI and associated technologies to leverage and build agents for workflow automation and observability
• 7) Industry certifications in NVIDIA infrastructure, public cloud providers, Data Science, etc. are a plus
• 8) Strong problem-solving, troubleshooting, communication, and collaboration skills to deliver reliable, scalable, and high-performance infrastructure solutions in fast-paced, dynamic environments that reward technical talent

15 years full time education

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