Lead Machine Learning Engineer & Lead Systems AI Engineer

• Agentic AI — Design, build and operate production-grade AI agent systems, covering the full lifecycle from cognitive architecture through observability, telemetry and rigorous testing and evaluation protocols.
• Cloud architecture & software engineering — Conceive, build and operate complex, scalable cloud architectures composed of distributed services and microservices. Drive enterprise-grade software development and take responsibility for the full Software Development Lifecycle (SDLC), from architectural design through deployment and operation.
• Data engineering & governance — Design and operate end-to-end data pipelines and data products for structured and unstructured data, including geospatial data where relevant. Establish and maintain solid data governance standards to ensure quality, security and compliance across the data landscape.
• AI & machine learning applications — Apply machine learning and AI methods to real-world problems and bring them from prototype into reliable production use.
• Technical leadership — Provide technical direction to GEG (and external) scientists and engineers, set architectural standards, mentor team members and orchestrate collaboration across multiple teams and stakeholders.
• Project management & strategic delivery — Steer multi-year strategic projects, including roadmap design and budget responsibility; deliver complex, cross-functional initiatives on time and within scope.

You are a lead engineer who combines a strong scientific foundation with substantial hands-on experience in AI, data, and cloud engineering, and who is ready to take both technical and organizational ownership of complex initiatives. The successful candidate brings:

• A Master's degree or PhD in (Geo-)Physics, Mathematics, Computer Science or a closely related quantitative discipline
• 5+ years of professional experience in software engineering, data engineering or a comparable technical role
• Proven experience in technical and people leadership, including guiding engineers, setting architectural direction, establishing engineering standards and mentoring team members
• Solid project management skills, including roadmap design, budget responsibility and the orchestration of cross-functional collaboration across multiple teams and stakeholders
• Hands-on experience in the development and operation of AI agents, including the surrounding tooling, orchestration, observability, evaluation, and monitoring in production
• Strong practical experience applying machine learning and AI methods to real-world problems, from prototyping through deployment
• Strongly preferred: deep, hands-on expertise in cloud-native development and operations on Google Cloud Platform (GCP), including the design and operation of complex architectures composed of distributed services and microservices
• Substantial experience in data engineering, including:
  • design of data models for structured and unstructured data
  • implementation and operation of robust, scalable data pipelines
  • establishing and maintaining data governance standards (quality, security, compliance)
• Excellent communication skills in English; German is a plus

Desirable skills and experiences

• Practical experience in the modeling, processing, and analysis of geospatial data
• Familiarity with numerical simulations and scientific computing workflows
• Experience working at the interface between research and software development
• Familiarity with MLOps practices, infrastructure as code and modern CI/CD workflows
• Contributions to open-source scientific, AI or data engineering projects
• Familiarity with SaaS implementation