AI Lead DevOps Engineer

At Virtusa (former ITMAGINATION), the company combines engineering excellence, creativity, and an AI-first mindset to co-create solutions that help businesses grow faster, operate smarter, and improve experiences with technology.

• 8–10 years of experience in DevOps/Cloud Engineering, with at least 3 years in a technical leadership or architect-level role.
• Deep understanding of the end-to-end ML lifecycle (training, validation, deployment, and retraining loops).
• Mastery across Azure DevOps, GitHub Actions, and Jenkins.
• Expert-level Terraform or CloudFormation skills, including modular architecture and cross-account cloud deployments.
• Significant experience implementing SAST/DAST tools and managing complex IAM/Access Control frameworks in a cloud environment.
• Ability to design custom observability frameworks that track model drift, pipeline failures, and infrastructure ROI.
• Advanced knowledge of configuration management tools like Ansible or Puppet for complex multi-cloud environments.
• Solid understanding of database scaling and security for MySQL, PostgreSQL, and MongoDB.
• Understanding of how DevOps practices support responsible AI (e.g., bias tracking and audit logs).
• Exceptional ability to collaborate with Architects and Data Scientists to translate high-level AI needs into operational reality.
• Native or C1-level English, with the ability to present technical strategies to senior stakeholders.

Strategic Leadership:

• Provide technical direction for the DevOps squad, defining the CI/CD and MLOps roadmap for the account.

Model Governance & Evaluation:

• Implement automated model evaluation pipelines to track accuracy, precision, and recall metrics in production.

Enterprise Security:

• Lead the DevSecOps strategy, ensuring all AI deployments comply with enterprise security standards and global data regulations.

Platform Enablement:

• Architect self-service platforms that allow ML engineers to deploy models with minimal friction while maintaining strict governance guardrails.

Auditability & Reproducibility:

• Ensure that every ML experiment is fully auditable through sophisticated pipeline and dataset versioning strategies.

Mentorship:

• Mentor senior and junior engineers, driving best practices in automation, IaC, and cloud-native architecture.