Description

What You'll Do:

• Design and implement security controls for AI, Machine Learning, and cloud-native applications.
• Integrate DevSecOps practices across development, deployment, and operational workflows.
• Secure AI/ML pipelines, models, datasets, APIs, and deployment environments.
• Conduct security assessments of AI models, infrastructure, and applications.
• Identify and mitigate vulnerabilities, threats, and risks associated with AI and ML systems.
• Implement secure CI/CD pipelines and automated security validation processes.
• Collaborate with architects, developers, DevOps engineers, and business stakeholders to ensure secure solution delivery.
• Define security standards, governance frameworks, and best practices for AI platforms.
• Perform threat modeling, security testing, code reviews, and architecture assessments.
• Support compliance, audit, regulatory, and risk management initiatives.
• Monitor security events, incidents, and vulnerabilities across AI and cloud environments.
• Drive security automation and implement security monitoring solutions.
• Troubleshoot security issues and conduct root cause analysis for incidents and vulnerabilities.
• Create technical documentation, security guidelines, operational procedures, and security playbooks.
• Contribute to engineering standards, reusable security frameworks, and continuous improvement initiatives.
• Support Agile delivery processes and participate in architecture reviews and production readiness assessments.

Expertise You'll Bring:

• 8 to 12 years of experience in Cyber Security, DevSecOps, Application Security, or Cloud Security.
• Strong hands-on expertise in DevSecOps practices and security automation.
• Experience securing AI, Machine Learning, and data-driven platforms.
• Strong understanding of ML Security concepts including model protection, adversarial attacks, data poisoning, and model governance.
• Experience implementing security controls across cloud-native and distributed application architectures.
• Hands-on experience with CI/CD tools and secure software delivery pipelines.
• Knowledge of container security, Kubernetes security, and cloud security practices.
• Experience conducting vulnerability assessments, penetration testing reviews, and security audits.
• Understanding of Identity and Access Management (IAM), secrets management, and privileged access controls.
• Experience with security monitoring, SIEM platforms, and incident response processes.
• Strong understanding of application security principles, secure coding practices, and threat modeling.
• Familiarity with AI governance, responsible AI frameworks, and regulatory compliance requirements.
• Knowledge of cloud security services and security architecture best practices.
• Strong analytical, troubleshooting, and risk assessment capabilities.
• Experience working in Agile and cross-functional engineering environments.
• Excellent communication, stakeholder management, documentation, and leadership skills.
• Ability to make practical technical decisions and drive security improvements across enterprise platforms

Key Skills

Education

Any Graduate