Cloud & GenAI Ops Architect

About the job

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.