**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 Accountabilities** + Pipeline & 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.