Lead the AI platform engineering team, manage roadmaps, projects, and team performance while ensuring platform reliability and compliance.
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
Qualifications
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.
Top Skills
Ai-Ops
Ci/Cd
Gitops
Gurobi
Hpc Scheduling
Infrastructure-As-Code
Llmops
Mlops
Rstudio Workbench
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