▷ (Only 24h Left) Solutions Architect

Job Purpose

Role Summary :

Responsible for architecting, designing, and implementing GPU-enabled, High Performance Computing (HPC), and AI / ML platform solutions. This role involves building scalable, secure, and optimized platforms to support enterprise AI, ML / DL workloads, simulation, and large-scale data analytics. The architect will define the infrastructure strategy, workload placement, performance optimization, and managed services roadmap for GPU and HPC platforms within the Data Centre (DC) business.

Role Description

Key Responsibilities :

• Platform Architecture & Design :
• Architect GPU and HPC infrastructure platforms for AI / ML training, inference, and HPC workloads.
• Design GPUaaS (GPU-as-a-Service) models, including on-demand, reserved, and burst GPU clusters.
• Integrate AI / ML frameworks (TensorFlow, PyTorch, KubeFlow, JupyterHub, etc.) into enterprise-ready stacks.
• Infrastructure & Workload Optimization :
• Ensure performance tuning, resource scheduling, and workload orchestration across HPC clusters and GPU nodes.
• Optimize for distributed training, model parallelism, and storage bandwidth (NVMe, Lustre, GPFS, Ceph).
• AI / ML Platform Enablement :
• Provide cloud-native environments with containerized ML workflows (Kubernetes, Docker, Singularity).
• Build and manage model hosting & inference platforms (REST APIs, containerized inference servers).
• Security & Compliance :
• Implement data security, encryption, access control, and compliance frameworks for sensitive AI / HPC workloads.
• Architect air-gapped solutions for government / defense workloads when required.
• Technology Integration & Innovation :
• Evaluate and integrate next-gen GPUs (NVIDIA H200 / A100 / L40S, AMD MI300, etc.), HPC accelerators, and AI chipsets.
• Enable hybrid / hyperconverged AI infrastructure (GPU + CPU + storage).
• Customer & Business Enablement :
• Collaborate with data scientists, researchers, and enterprise customers to align platform capabilities with business outcomes.
• Define GPU / HPC platform services catalog and managed service offerings.
• Automation & DevOps :
• Implement MLOps pipelines, infrastructure as code (Terraform, Ansible), and workload scheduling (SLURM, Kubernetes).

Experience & Educational Requirements

Qualifications and Experience

EDUCATIONAL QUALIFICATIONS : (degree, training, or certification required)

BE / B-Tech or equivalent with Computer Science or Electronics & Communication

RELEVANT EXPERIENCE : (no. of years of technical, functional, and / or leadership experience or specific exposure required)