Job Description

Do you want to help solve the world's most pressing challenges? Feeding the world's growing population and slowing climate change are two of the world's greatest challenges. AGCO is a part of the solution! Join us to make your contribution. 
 
As an AI Platform Architect, you will define and evolve the architecture of AGCO’s AI platform, designing the technical foundation that empowers teams to deliver AI solutions efficiently, securely, and with confidence. Your work will shape how ML models move from experimentation to production, how AI platform services are consumed across teams, and how platform capabilities scale to support advanced use cases on cloud and edge deployments, including onboard our machines in the field.   
 
Your Impact 

• Define the reference architecture for AGCO’s AI platform, covering AI/ML data pipeline platforms, model training infrastructure, CI/CD for ML, artifact management, observability, and self-service developer tools. Ensure platform services are scalable, auditable, and cost-efficient across heterogeneous workloads, e.g., computer vision, GenAI, machine learning, etc. 

• Design core platform services such as containerized training environments, experiment tracking, model registries, and reusable orchestration patterns. 

• Architect integration interfaces (API/CLI/UI) that allow AI delivery teams to self-serve platform capabilities reliably and securely. 

• Collaborate with Enterprise Architecture, AI PODs and Product Engineering teams to ensure interoperability across systems. Support model deployment across cloud, internal APIs, dashboards, and embedded systems in agricultural machinery. 

• Establish technical guardrails for reusability, performance, and lifecycle management of models and agents. Serve as a technical leader and advisor across teams, contributing to strategy, roadmap, and engineering excellence 

 
Your Experience and Qualifications 

• 10+ years of experience in Software-, ML infrastructure- or Platform engineering, including 3+ years in AI platform architecture. Proven success designing and deploying enterprise-grade ML infrastructure and AI platforms 

• Deep expertise in cloud-native technologies and principles (GCP), e.g. Vertex AI, Cloud Run, GKE, Pub/Sub and Artifact Registry as well as automation, elasticity and resilience by default 

• Experience with CI/CD for ML using tools like GitHub Actions, Kubeflow, and Terraform. Strong knowledge of containerization, reproducibility, and secure environment management (e.g. Kubernetes, AWS ECS, Azure Service Fabric and Docker) 

• Deep understanding of model lifecycle management, including training, versioning, deployment, and monitoring. Familiarity with data and ML orchestration tools (e.g., Airflow), feature stores, and dataset management systems. 

• Excellent systems thinking and architectural design skills, with the ability to design for modularity, scalability, and maintainability. Proven ability to work cros