DevSecOps Engineer

• 5+ years of professional experience in DevSecOps, Application Security, Infrastructure Security, or a closely related area.
• Strong programming and scripting skills in Python and Linux Shell Scripting.
• Practical experience implementing and operating: Static Application Security Testing (SAST), Dynamic Application Security Testing (DAST), vulnerability management, dependency and software composition security, and container security.
• Strong knowledge of Application Security and Infrastructure Security concepts and practices.
• Proven experience embedding security controls and automated security checks into Azure DevOps pipelines.
• Understanding of secure software development principles and integrating security into CI/CD processes.

Nice to have:

• Familiarity with AI Security, including LLM-related threats, model security, AI application risks, and secure AI development.
• Experience securing Kubernetes clusters and containerized workloads.
• Knowledge of Infrastructure as Code (IaC), with Terraform preferred.
• Experience with Microsoft Azure and cloud security practices.
• Practical knowledge of container and cloud security.
• Experience creating or maintaining Ansible automation scripts.

• Build, implement, and continuously improve DevSecOps processes across development and deployment workflows.
• Integrate and operate security testing solutions such as Static Application Security Testing (SAST), Dynamic Application Security Testing (DAST), dependency scanning, vulnerability assessment, and container security tools.
• Create and maintain security automation solutions using Python and Linux Shell Scripting.
• Conduct security reviews and assessments of applications and infrastructure, and coordinate remediation of identified risks.
• Work closely with development, infrastructure, operations, and security teams to incorporate security requirements throughout the SDLC.
• Implement, maintain, and improve security controls within Azure DevOps CI/CD pipelines.
• Contribute to the secure implementation of AI/ML technologies, including AI security assessments, governance, and risk management.
• Define and maintain security guidelines, standards, policies, and best practices for cloud, DevOps, and AI-enabled environments.
• Track new and emerging security threats and translate them into appropriate security controls, mitigations, and engineering improvements.