Cloud Solution Architect – Enterprise AI Infrastructure & FinOps

We are looking for a Cloud Solution Architect whose primary responsibility is to design, build, manage, and optimize the enterprise cloud infrastructure that runs our AI ecosystem.

Job Description

Role

Cloud Solution Architect – Enterprise AI Infrastructure & FinOps

Experience: 12–18 years total, with at least 6 years in cloud architecture and at least 3 years architecting production AI/ML or GenAI platforms

Domain

Enterprise AI Platform (Pharma / Life Sciences, GxP-aware)

Education: B.E./B.Tech in Computer Science, Information Technology, Information Security, Electronics & Communication, or related Engineering disciplines.

Location

Ahmedabad/Mumbai

Role Summary

The individual hired as Cloud Solution Architect will be responsible for cloud foundation end to end: landing zones, networking, compute, storage, identity, security, Kubernetes platforms, GPU capacity, infrastructure-as-code, monitoring, resilience, and day-to-day infrastructure operations.

The role will ensure that AI and GenAI workloads operate securely, reliably, and at scale while driving cloud governance, operational excellence, and FinOps practices to maintain transparency, control, and optimization of cloud and AI spending.

This is a hands-on infrastructure leadership role. Application and AI engineering teams build agents and workflows; you make sure the platform they run on is secure, stable, scalable, compliant, and cost-efficient.

Key Responsibilities

Cloud Infrastructure Architecture

Own the enterprise cloud infrastructure architecture on Azure, AWS, and/or GCP, including multi-cloud and hybrid designs.

Design and maintain enterprise landing zones: management group and account/subscription structure, policies, guardrails, naming and tagging standards, and environment separation (dev, test, validation, production).

Architect network infrastructure: hub-and-spoke or Virtual WAN topologies, VNets/VPCs, subnets, private endpoints, DNS, load balancers, application gateways, WAF, ExpressRoute/Direct Connect, site-to-site VPN, and hybrid connectivity to data centers and manufacturing sites.

Design compute platforms across VMs, VM scale sets, containers, Kubernetes (AKS, EKS, GKE), serverless (Functions, Lambda, Cloud Run), and managed PaaS services.

Architect storage and database infrastructure: object storage, file shares, managed disks, backup vaults, and managed databases (SQL, PostgreSQL, Cosmos DB, DynamoDB, Redis).

Design for high availability, multi-region resilience, disaster recovery, and business continuity with defined RTO and RPO targets.

Maintain architecture documentation, reference designs, and architecture decision records (ADRs).

Cloud Infrastructure Management and Operations

Own the day-to-day health, stability, and performance of cloud infrastructure supporting AI and enterprise workloads.

Lead infrastructure provisioning, configuration, patching, upgrades, and lifecycle management for VMs, Kubernetes clusters, networking, and platform services.

Manage Kubernetes platform operations: cluster upgrades, node pools (CPU and GPU), ingress, service mesh, autoscaling, namespaces, and multi-tenancy for AI teams.

Define and track infrastructure SLOs, SLAs, and capacity plans, and lead major incident response, root cause analysis, and problem management.

Implement backup, restore, and DR testing on a regular schedule and maintain runbooks.

Establish ITIL-aligned processes for incident, change, problem, and service request management in collaboration with IT operations and managed service providers.

Manage cloud vendors and managed service partners, including SLA and performance governance.

Drive operational automation to reduce manual effort and human error.

• Infrastructure-as-Code, Automation and Platform Engineering

Define and enforce infrastructure-as-code standards using Terraform, Bicep/ARM, Pulumi, or CloudFormation.

Build reusable IaC modules, templates, and "golden paths" so teams can self-serve compliant infrastructure quickly.

Implement CI/CD and GitOps for infrastructure (GitHub Actions, Azure DevOps, GitLab, Argo CD, Flux).

Apply policy-as-code (Azure Policy, AWS SCPs/Config, OPA/Gatekeeper) to prevent misconfiguration and enforce security and cost guardrails.

Automate environment provisioning, scaling, patching, compliance checks, and cleanup.

Build an internal developer platform experience for AI, data, and application teams.

AI Infrastructure Enablement

Design and operate the infrastructure foundation for AI workloads, covering agent runtimes, workflow engines, data pipelines, model serving, and vector and search services.

Provision and manage GPU and accelerator capacity (NVIDIA A100/H100/L40S, Inferentia/Trainium, TPUs) for inference, fine-tuning, and batch processing, including quota management and capacity reservations.

Host and secure LLM access infrastructure: Azure OpenAI, AWS Bedrock, Vertex AI, and self-hosted open-source models (vLLM, TGI, Triton, KServe, Ray).

Deploy and operate a centralized AI/LLM gateway for secure model access, routing, rate limiting, caching, logging, and cost attribution.

Provide infrastructure for agent frameworks and orchestration tools (e.g., LangGraph, Semantic Kernel, Azure AI Foundry, Bedrock Agents, Temporal, Airflow, Logic Apps, Step Functions), including MCP servers and secure tool connectors.

Support data platforms used by pipelines, such as Databricks, Snowflake, Microsoft Fabric, Synapse, Kafka/Event Hub, and vector stores (Azure AI Search, OpenSearch, pgvector, Pinecone, Qdrant).

Ensure network isolation, private connectivity, and secure integration between AI services and enterprise systems (SAP, LIMS, MES, QMS, document repositories, and on-premises data).

Partner with AI engineering teams on LLMOps/MLOps infrastructure: model registries, experiment tracking, CI/CD for AI assets, and environment promotion.

Architect the agent runtime platform: hosting, lifecycle management, state and memory,