Manager, Platform Engineering, AI & Datascience

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  

• • Chair design reviews to enforce architecture standards, security controls, and cost-efficiency patterns. 

• 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). 

• • Introduce self-service tooling that reduces manual ops toil and speeds developer onboarding. 

• Stakeholder & Customer Engagement 

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

• • Provide transparent status updates, KPI dashboards, and quarterly roadmap demos. 

• 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: 6 years relevant experience. 

• Typical requirement: 7–10 years total experience, with 3+ years running production MLOps/LLMOps or HPC environments and 2+ years managing engineers.