We are looking for a highly skilled and motivated Senior AI Engineer (m/f/d) to own and advance the shared infrastructure and tool stack supporting AI development, experimentation, analysis and delivery. In this role, you will combine expertise in MLOps, software architecture and platform engineering to create reliable, secure and scalable capabilities that accelerate AI teams across the organization. You will work closely with AI, Data, Analysis and Software specialists to identify recurring challenges, establish common standards and translate development needs into reusable services, automation and self-service workflows. You will also help introduce governed agentic capabilities where they provide clear value. The ideal candidate combines strong engineering expertise with a practical understanding of machine learning, a platform-oriented mindset and the ability to drive technical alignment across teams.
Your responsibilities will include:
- Own the architecture, roadmap and continuous improvement of the shared tool stack supporting AI development, experimentation, analysis and delivery across teams.
- Maintain the reliability, usability, security and scalability of the platform, including its core services, integrations and development workflows.
- Design and implement reusable services, APIs, interfaces and automation covering experimentation, model evaluation, data processing, inference and analytical workflows.
- Define and promote common engineering standards for testing, reproducibility, versioning, observability, documentation and operational readiness.
- Identify recurring technical needs and bottlenecks, then translate them into practical platform improvements and self-service capabilities.
- Partner with AI, Data, Analysis and Software teams to support adoption, troubleshoot complex issues and ensure the tool stack addresses real development and modelling needs.
- Use representative machine-learning workflows to validate platform capabilities and contribute hands-on to model integration or development where required.
- Introduce governed agentic capabilities, such as MCP integrations and tool-assisted workflows, where they provide value while maintaining access controls, traceability and human oversight.
