The Senior AI Platform Engineer designs and operates an MLOps/LLMOps platform, focusing on CI/CD pipelines, observability, compliance, and coaching teams on scalable patterns.
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
Qualifications
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
Top Skills
AWS
Ci/Cd
Commercial Tools
Github Actions
Gpu Orchestration
Llmops
Mlops
Oss Tools
Responsible-Ai
Terraform
Vector Databases
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