Azure Devops & Mlops Engineer

Key Responsibilities
1. 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.
2. 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.
3. Security & Compliance
Enforce governance guardrails for both IT and AI workloads.
Ensure adherence to regulatory and internal security standards.
4. 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.
Hands on with Azure DevOps (Repos, Pipelines, Artifacts), GitHub Actions, and YAML based 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)
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.