Job Purpose and Impact

The Senior AI Platform Engineer for AI Ops in AI & Data Science designs, builds and operates the shared MLOps / LLMOps platform that powers Cargill’s data-science and GenAI products. You will own CI/CD pipelines for data ingestion, model training, evaluation and deployment; automate GPU/CPU orchestration across clouds; and embed Responsible-AI, observability and cost-optimization into every stage of the lifecycle. Success is measured by model-to-production velocity, platform uptime, and total-cost-of-ownership improvements 

Key AccountabilitiesPipeline & Automation Implement and maintain reproducible pipelines for data ingestion, feature engineering, model training and deployment using OSS and Commercial toolchains;  Create Terraform modules and GitHub Actions to enable one-click environment provisioning. GenAI / LLMOps / AgentOps Enablement Extend platform to support retrieval-augmented generation (RAG) workflows, AI agent workflows, vector databases (pinecone etc), prompt evaluation harnesses, and guardrail policies Develop automation scripts for GenAIOps.        Observability & SRE: Instrument Service Level Indicator/Objective (SLIs/SLOs), build dashboards, and lead on-call runbooks;  Monitor system performance and troubleshoot production issues to achieve low latency and availability. Security & Compliance Embed IAM, secrets-management, lineage tracking and Responsible-AI checks into pipelines Produce SOC-2 / ISO-27001-ready documentation.      Coaching & Continuous Improvement Review pull-requests, run blameless post-incident reviews, and mentor data-science teams on scalable MLOps patterns. 

Qualifications

Minimum: 4 years hands-on building ML or data platforms. 

Typical: 5–8 years total, including 2 + years operating production MLOps/LLMOps or GPU-accelerated workloads in AWS.