AI Lead DevOps Engineer

O projekcie: At Virtusa( former ITMAGINATION), every innovator has the potential to transform and lead in a digital world—but unlocking that potential takes more than technology; it takes a trusted partner who combines engineering excellence, creativity, and an AI-first mindset. Together, we co-create solutions that help businesses grow faster, operate smarter, and make experiences better with technology. We are looking for an AI Lead DevOps Engineer to spearhead the MLOps strategy for our high-impact AI accounts. With 8–10 years of experience, you will provide the technical leadership necessary to design robust, compliant, and highly automated AI platforms. You aren't just managing pipelines; you are architect the entire lifecycle governance—ensuring reproducibility, audibility, and security at an enterprise scale. Wymagania: - 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. Codzienne zadania: - 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.