Principal, AI & Data Science- AI/ML Engineer

Cargill, Inc.•Atlanta, GA

60d

About The Position

Cargill's size and scale allows us to make a positive impact in the world. Our purpose is to nourish the world in a safe, responsible and sustainable way. We are a family company providing food, ingredients, agricultural solutions and industrial products that are vital for living. We connect farmers with markets so they can prosper. We connect customers with ingredients so they can make meals people love. And we connect families with daily essentials - from eggs to edible oils, salt to skincare, feed to alternative fuel. Our 160,000 colleagues, operating in 70 countries, make essential products that touch billions of lives each day. Join us and reach your higher purpose at Cargill. Job Purpose and Impact The Advisor, AI & Data Science job provides thought leadership in the development and implementation of artificial intelligence solutions that drive strategic decision making. As a recognized subject matter expert in the field, this job partners to develop the organization's artificial intelligence strategy, leads implementation of related platforms and products to drive business value and encourages a culture of compliance to governance framework. This job finds opportunities to improve processes and leads efforts to ensure quality of the outcomes and measurable business impact.

Requirements

Minimum requirement: 6 years relevant experience.
Typical requirement: 10 + years in software/data engineering with at least 4 years running production ML or HPC workloads and 2 + years in GenAI/LLMOps. Experience leading cross-functional engineering teams preferred.

Responsibilities

Technical Strategy & Architecture:
Define multi-year AI Ops roadmap covering model experimentation, HPC scheduling, inference serving, and data lineage.
Evaluate and integrate best-fit OSS/commercial tooling
Platform Engineering & Operations
Build and maintain CI/CD pipelines for model training (GPU/CPU), feature engineering, and automated testing across cloud and HPC clusters.
Implement scalable vector databases and caching layers to support low-latency GenAI workloads.
Tune scheduler policies for optimal occupancy and cost.
Monitor system performance, investigate escalations, and lead post-incident reviews.
Governance & Compliance
Champion data-privacy, model-risk-management, and export-control requirements; embed policy checks into pipelines.
Deliver audit-ready documentation for SOC-2, ISO 27001, and sector-specific regulations (e.g., food safety, trade compliance).
Leadership & Mentoring
Coach senior engineers and data scientists on scalable MLOps/HPC patterns.
Lead AI Platform Design Reviews and contribute to internal communities of practice.