**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** + Design & Build + Develop multi-agent workflow automation patterns using Agentic AI + Process redesign and mapping to agentic workflow patterns + Architect scalable micro-services that wrap LLM/RAG/Agent workflows (Python). + Implement robust prompt-engineering patterns, retrieval pipelines, and caching for AI Assistants and AI Agents + Platform Ops + Extend evaluation, automated testing, canary rollout, and rollback for AgentOps. + Profile inference latency, GPU/CPU utilization, and memory; deliver quarterly cost-to-serve reductions + Operational Excellence + Own on-call runbooks, SLOs, and incident reviews; embed observability. + Enablement & Mentoring + Coach full-stack and data-science peers on GenAI/LLMOps patterns; create internal workshops and tech blogs. **Qualifications** + Minimum: 4 years building production software or data platforms . + Typical: 5–8 years, including 2+ years with cloud-native AI/ML or GenAI systems (Azure, AWS, or GCP) or 2+ years of software devlopment