Azure Devops Lead

Role Overview This position bridges IT infrastructure management and Azure DevOps engineering to support both AI/ML workloads and enterprise IT services. The role ensures secure, scalable, and compliant delivery of applications, data pipelines, and machine learning models across Azure cloud environments.

Key Responsibilities

• DevOps & Automation ? Design and maintain Azure DevOps pipelines (YAML) for build, test, security scanning and release of AI/ML models.
• ? Implement Infrastructure as Code (IaC) using ARM templates, Terraform, or Bicep.
• ? Manage artifact repositories and container image signing for compliance.
• Cloud & Platform Management ? Administer Azure environments, including AKS clusters for containerized workloads and GPU-enabled pools for ML training.
• ? Standardize environments for business applications and Azure Machine Learning workspaces.
• Security & Compliance ? Enforce governance guardrails for both IT and AI workloads.
• ? Ensure adherence to regulatory and internal security standards.
• Collaboration & Support ? Partner with data science teams to enable rapid deployment of ML models.
• ? Work with IT stakeholders to maintain reliability and scalability of enterprise systems.

Required Qualifications

• ? 5+ years in DevOps/SRE, with 3+ years on Azure.
• ? Handson with Azure DevOps (Repos, Pipelines, Artifacts), GitHub Actions, and YAMLbased CI/CD.
• ? Strong IaC skills (Terraform or Bicep/ARM); experience managing AKS and container registries (ACR).
• ? Proficiency with PowerShell and/or Python for automation; familiarity with Docker and Helm.
• ? Experience operating Azure Machine Learning (workspaces, endpoints, registries) and integrating ML workflows in CI/CD.
• ? Solid understanding of cloud security (RBAC, Key Vault, Managed Identity, policies), networking (VNet, subnets, Private Link), and cost management

Required Skills

• ? Strong experience with Azure DevOps, AKS, and Azure Machine Learning.
• ? Proficiency in CI/CD, IaC, and container orchestration.
• ? Familiarity with cloud security best practices and compliance frameworks.
• ? Knowledge of AI/ML deployment workflows and traditional IT infrastructure.

Preferred Qualifications

• ? Experience with GPU workloads on AKS or AML; model serving (Azure ML Managed Online Endpoints, Kubernetes Inference).
• ? Familiarity with Databricks, Fabric, or other data platforms that feed AI services.
• ? Certifications: Azure DevOps Engineer Expert, Azure Solutions Architect, or equivalent.

Core Tools & Technologies

• ? Azure: AKS, Functions, Storage, Key Vault, Monitor/Log Analytics, API Management, Event Hub/Service Bus.
• ? DevOps: Azure DevOps, GitHub, ACR, Helm, Docker; Terraform or Bicep/ARM.
• ? Security/Governance: Azure Policy, RBAC, Managed Identity, Key Vault, Defender for Cloud.
• ? Observability: Application Insights, Azure Monitor, Grafana/Prometheus.
• ? Languages: YAML, PowerShell, Python; optional Bash.

Deliverables (First 90 Days)

Soft Skills

For applications and inquiries,

• ? Baseline CI/CD templates for AI services (build, test, security scan, release) and documented promotion strategy.
• ? Reference AKS/AML environment deployed via IaC with guardrails (RBAC, policies, private networking) and runbooks.
• ? Unified observability stack with dashboards, alerts, and SLOs for pipeline and platform health.
• ? Playbooks for incident response and change management; backlog of automation opportunities.