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:

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

RELEVANT EXPERIENCE:

• Experience

  : 8–12 years overall IT experience, with

  5+ years in HPC/AI/ML/GPU platform architecture

  .

• Technical Expertise

  :
• Strong background in

  GPU architecture (NVIDIA, AMD)

  and

  HPC systems

  .
• Proficiency in

  AI/ML frameworks

  (TensorFlow, PyTorch, Keras, MXNet, Hugging Face).
• Experience with

  distributed training and orchestration frameworks

  (KubeFlow, MLflow, Ray, Horovod).
• Knowledge of

  parallel computing, MPI, CUDA, ROCm, and GPU drivers

  .
• Familiarity with

  storage technologies

  for HPC/AI (NVMe, Lustre, GPFS, Ceph, Object Storage).

• Cloud & Hybrid AI Platforms

  : Hands-on with

  GPU cloud offerings

  (AWS Sagemaker, Azure ML, GCP Vertex AI) and

  on-prem HPC cluster management

  .

• Automation & MLOps

  : Experience with

  CI/CD for ML (MLOps)

  , workflow automation, and

  infrastructure as code

  .

• Security & Governance

  : Knowledge of

  data privacy, DPDP Act, compliance (ISO, PCI-DSS, HIPAA)

  , and secure GPU cluster design.

• Certifications (Preferred)

  : NVIDIA Certified AI Specialist, Azure AI Engineer, AWS ML Specialty, or HPC-related certifications.

• Soft Skills

  : Strong stakeholder communication, ability to collaborate with

  data scientists, researchers, and enterprise IT teams

  , and capability to align technical solutions to business objectives.