Senior AI Platform Engineer

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.