Azure DevSecOps Engineer
Skills
About This Role
Overview
We are seeking a highly skilled Azure DevSecOps Engineer with a strong background in AI/ML to design, implement, and secure scalable cloud-native solutions.
The ideal candidate will integrate DevOps, security, and MLOps practices to enable secure and efficient delivery of AI-driven applications on Azure.
DevSecOps & Cloud Engineering
- Design and implement CI/CD pipelines using Azure DevOps, GitHub Actions
- Implement Infrastructure as Code using Terraform / Bicep / ARM templates
- Manage containerized workloads using Docker and Kubernetes (AKS)
- Establish monitoring, logging, and observability frameworks
Security & Compliance (DevSecOps)
- Embed security controls across CI/CD pipelines (SAST, DAST, SCA, container scanning)
- Implement secure identity and access management using Azure AD (RBAC, Managed Identities)
- Define and enforce cloud security baselines, policies, and governance
- Conduct vulnerability management, threat modeling, and risk assessments
- Ensure compliance with industry standards and regulatory frameworks
AI/ML & MLOps
- Build and manage ML pipelines using Azure Machine Learning
- Automate model lifecycle: training, validation, deployment, and monitoring
- Implement model governance, versioning, and auditability
- Secure ML pipelines and data workflows (data privacy, model integrity)
Security Standards & Frameworks (Required Exposure)
- Hands-on experience or strong understanding of:
- OWASP Top 10 (API & Web Security Risks)
• NIST Cybersecurity Framework (CSF)
- ISO/IEC 27001 & ISO 27002 controls
- CIS Benchmarks for Azure
- SOC 2 Type II compliance
- GDPR / Data privacy regulations (as applicable)
- Zero Trust Architecture principles (identity-first security model)
Required Skills & Qualifications
- Strong experience with Microsoft Azure cloud platform
- Expertise in CI/CD pipelines and DevSecOps tooling
- Experience with containerization and orchestration (Docker, Kubernetes/AKS)
- Proficiency in scripting (Python, PowerShell, Bash)
- Hands-on experience with security tools (e.g., Snyk, SonarQube, Checkmarx, Microsoft Defender for Cloud)
- Understanding of AI/ML lifecycle and MLOps practices
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