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.