Role

We are seeking an AI Platform Engineer – MLOps & Cloud to help design, build, and operate ML operations and agentic pipelines for large-scale AI systems. Your work will enable reliable, automated, and production-grade workflows for training, evaluating, and deploying intelligent agents.

• Architect and maintain ML workflows and agentic pipelines end-to-end.
• Integrate model execution, data flows, evaluation loops, and orchestration into robust production systems.
• Build scalable infrastructure across cloud environments.
• Develop CI/CD and CT (continuous training) pipelines for ML workloads.
• Ensure system reliability, observability, and security.
• Collaborate closely with ML, software, and DevOps teams to optimize workflows and reduce time to production.
• Automate infrastructure provisioning, configuration, and deployment processes.

Required Skills

• Bachelor+Master degree in Computer Engineering or the like.
• Strong Python skills for tool automation and workflow orchestration.
• Strong experience with cloud environments (Azure, AWS, GCP).
• Familiarity with cloud ML services and hybrid cloud architectures (AWS SageMaker, Vertex AI, ML).
• Hands-on experience deploying ML systems into production.
• Experience with containers and Kubernetes. Experience with Infrastructure-as-Code and CI/CD practices.
• Strong understanding of networking, security, and system monitoring.
• Ability to work both independently and collaboratively with ML and engineering teams.
• Proficient in English.

Desirable Skills

• Experience in banking or other regulated environments.
• Experience designing or maintaining agentic systems, distributed training setups, or inference services.
• Experience with monitoring and log management.
• Proficiency in German, Italian, or French is a plus.

We offer

• We offer Full-time permanent contract.
• Competitive compensation and growth opportunities.
• Access to cutting-edge AI infrastructure and modern MLOps stacks. High autonomy and impact on production-grade AI systems.
• Mentorship and continuous learning.