DevOps Engineer (AI, GitLab)

We are looking for a DevOps Engineer (AI, GitLab) to join Sopra Steria Polska and one of our innovative international squads. You will be part of an exciting initiative in cutting edge technologies that bring about institutional transformation and impacts, by driving innovation and collaboration across network.

Note that we can only offer cooperation to people who are located in Poland and have EU citizenship.

Responsibilities:​

• Design and maintain AI infrastructure: Set up, configure and manage cloud, on-premises or hybrid environments needed for AI development, training and inference, including compute, storage, networking and GPU resources.
• Automate deployment pipelines: Build and maintain CI/CD and MLOps pipelines for code, data workflows, model training, testing, validation and deployment.
• Manage containerisation and orchestration: Use tools such as Docker and Kubernetes to package, deploy and scale AI applications and services reliably across environments.
• Support model operationalisation: Enable the transition of machine learning models from development to production, ensuring reproducibility, versioning, traceability and controlled releases.
• Monitor systems and model services: Implement monitoring, logging, alerting and performance tracking for infrastructure, applications and AI model endpoints, including availability, latency, resource usage and failures.
• Ensure reliability, scalability and performance: Optimise infrastructure and deployment processes so AI services can scale efficiently and remain stable under changing workloads.
• Implement security and compliance controls: Apply security best practices for infrastructure, access management, secrets handling, software dependencies, data protection and regulatory compliance.
• Manage environments and configuration: Standardise development, test and production environments using Infrastructure as Code and configuration management tools to ensure consistency and reproducibility.
• Collaborate across teams: Work closely with data scientists, AI engineers, software developers, cybersecurity specialists and business teams to support delivery of AI solutions.
• Support data and model lifecycle governance: Contribute to processes for artefact versioning, auditability, lineage, backup, recovery and lifecycle management of datasets and models.
• Troubleshoot and improve operations: Investigate incidents, deployment issues and performance bottlenecks, and continuously improve automation, resilience and operational efficiency.