AI Senior 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 seeking seeking an AI Senior DevOps Engineer to bridge the gap between AI development and production-grade operations. You will be responsible for building the automated "highways" that allow ML models to flow from training to deployment seamlessly. This role requires a strong DevOps foundation combined with an understanding of the unique challenges of MLOps, such as GPU resource management, model versioning, and performance monitoring. Wymagania: - 6–8 years of experience in DevOps/SRE roles, with a minimum of 2 years focused on MLOps or supporting AI/ML workloads.   - Deep expertise in Jenkins, GitHub Actions, or GitLab CI/CD.   - Hands-on proficiency with Azure DevOps and Terraform (CloudFormation is a strong plus).   - Advanced knowledge of Docker and Kubernetes for managing distributed AI applications.   - Proven experience setting up Prometheus and Grafana dashboards for technical and model-specific metrics.   - Practical experience managing or connecting to MySQL, PostgreSQL, or MongoDB.   - Familiarity with Ansible, Chef, or Puppet for automated environment setup.   - Hands-on experience with SAST/DAST tools and automated vulnerability scanning within CI/CD pipelines.   - Experience with versioning tools for datasets and models (e.g., DVC or similar pipeline versioning logic).   - Professional English (C1) for seamless interaction with global delivery teams. Codzienne zadania: - CI/CD & MLOps Pipelines: Build and maintain automated pipelines for ML models using Azure DevOps, GitHub Actions, or Jenkins. - Workflow Automation: Automate model validation, packaging, and deployment workflows to ensure rapid iteration cycles. - Infrastructure as Code (IaC): Use Terraform or CloudFormation to provision and manage cloud-native infrastructure, focusing on high-availability and scalability. - Monitoring & Observability: Set up comprehensive monitoring for infrastructure (CPU/GPU/Memory) and model performance (latency and drift) using Prometheus and Grafana. - DevSecOps Implementation: Integrate security into the heart of the pipeline, including secret management, IAM role configuration, and vulnerability scanning. - Collaboration: Work closely with Data Scientists and AI Engineers to enable a self-service platform for model deployment.