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)

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.