Cloud & GenAI Ops Architect

KPMG’s Digital & GenAI team builds practical, secure cloud and AI solutions for enterprise clients. We combine consulting, engineering and product delivery to help organizations adopt scalable, cost-effective AI and cloud platforms.

Role summary

We’re hiring a hands-on Cloud & GenAI Ops Architect who designs and runs production multi-cloud architectures (AWS / Azure / GCP), productionizes GenAI/ML systems and RAG pipelines, and leads client-facing delivery. This is an implementation + advisory role: you’ll produce architectures and runbooks, help teams implement them, and enable clients to operate and evolve the solution.

Key responsibilities

Design and deliver end-to-end multi-cloud solutions (architecture diagrams, runbooks, operational playbooks, runbooks for incident response and rollback).
Choose and implement the right compute patterns across clouds: static hosting, PaaS web apps, container platforms (AKS/GKE/EKS + Fargate), serverless (Azure Functions / Cloud Run / Lambda), VM scale sets and managed app services — and justify trade-offs to clients.
Productionize GenAI / MLOps: model serving, inference scaling, A/B / canary rollouts, versioning, drift detection, model metrics, and embedding/RAG pipelines.
Work with cloud AI platforms & model catalogs (e.g., Vertex AI / Model Garden and Azure AI Foundry) to evaluate, deploy and integrate models into products. (Google Cloud, Microsoft Azure)
Implement observability and diagnostics for ML and platform services (metrics, logs, traces, dashboards, alerts) using native tooling (AWS CloudWatch, Azure Monitor / Application Insights, Google Cloud Monitoring) and integrate with SIEM/incident workflows. (AWS Documentation, Microsoft Learn)
Build repeatable IaC and GitOps patterns (Terraform modules, Bicep/ARM, Helm, GitOps flows) and author CI/CD pipelines (Azure DevOps, GitHub Actions, GitLab CI, Jenkins) for secure, auditable deployments.
Integrate security & compliance with Dev and SecOps teams (IAM design, least-privilege, key management, network segmentation, encryption, audit trails).
Drive FinOps and cost governance (resource tagging, cost allocation, rightsizing, reservation strategies and budgets).
Mentor and enable client/internal teams with architecture reviews, runbooks, workshops, and handovers.
Participate in presales/technical scoping and produce pragmatic proposals and effort estimates.

Requirements:

Deep, demonstrable experience across AWS, Azure and GCP designing and operating production systems.
Strong production experience with Kubernetes (cluster ops, autoscaling, CNI networking, storage, multi-tenant considerations) and Terraform (module design, remote state, testing).
Broad knowledge of compute patterns: static web hosting, managed web apps, containers, serverless, VM scale sets and when to use each pattern in AWS/GCP/Azure.
Hands-on GenAI / MLOps experience: model serving, monitoring (model and infra), drift detection, embeddings, vector stores, and RAG pipelines.
Familiarity with cloud model platforms / model catalogs and deployment flows (for example Google’s Model Garden on Vertex AI and Microsoft’s Azure AI Foundry). (Google Cloud, Microsoft Azure)
Observability competence: designing alerts, dashboards and SLO/SLI — experience with CloudWatch, Azure Monitor / Application Insights, or Google Cloud Monitoring. (AWS Documentation, Microsoft Learn)
Strong background in CI/CD (Azure DevOps, GitHub Actions, GitLab CI), container registries, image scanning, and secure build pipelines.
Security & compliance awareness: IAM, KMS/HSM, VNet/VPC design, audit logging and regulatory constraints.
Solid scripting/automation skills (Python, Bash; Go / PowerShell a plus).
Excellent client communication: can present trade-offs, lead workshops, and translate technical decisions to business stakeholders.
Proven problem solver with a can-do spirit — creative, pragmatic, able to design out-of-the-box solutions under constraints and pressure.

Nice to have

Cloud / platform certifications (AWS/Azure/GCP Architect, CKA/CKS, HashiCorp Terraform, FinOps).
Enterprise consulting experience and prior delivery in regulated industries.
Prior exposure to large LLM deployments, inference cost-management and hybrid/sovereign AI deployments.