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Job Purpose and Impact

The Manager for AI Platform Engineering leads the team that designs, builds, and operates Cargill’s enterprise AI-Ops platform—covering MLOps, LLMOps/GenAIOps, HPC scheduling, and optimisation services (e.g., Gurobi, RStudio Workbench). You will own the platform roadmap, allocate people and budget, drive project delivery, and embed best-in-class reliability, security, and compliance practices. Success is measured by platform uptime, model-to-production velocity, cost-to-serve trends, and team engagement.

Key Accountabilities

Platform Ownership & Road-mapping 

• Define and maintain the technical roadmap for MLOps, LLMOps, HPC, and optimization tooling 
• Oversee the portfolio of AI-Ops projects; align scope, schedule, and budget to business objectives. 

Technical Guidance & Governance 

• Champion infrastructure-as-code, GitOps, and CI/CD pipelines  

Quality, Reliability & Compliance 

• Set and monitor SLIs/SLOs for training, inference, and optimization services; lead post-incident reviews. 
• Ensure Responsible-AI guardrails, data-privacy, and license-management policies are implemented. 

Process Improvement & Automation 

• Drive continuous-improvement initiatives (test-driven development, auto-scaling policies, cost dashboards). 

Stakeholder & Customer Engagement 

• Partner with product managers, data-science leads, and security/compliance teams to capture requirements and set priorities. 

Team Management & Talent Development 

• Set performance objectives, conduct regular feedback and coaching sessions, and create growth plans. 
• Foster an inclusive culture that values experimentation, blameless post-mortems, and knowledge sharing

Qualifications

• Minimum requirement of 4 years of relevant work experience. Typically reflects 5 years or more of relevant experience.

Preferred: 

• 3+ years running production MLOps/LLMOps or HPC environments
• 2 + years managing engineers or cross-functional delivery teams