Job DescriptionTurn decades of data into intelligence that helps feed the world.VAS has decades of longitudinal data from some of the most productive dairy operations in the world. Our next challenge is turning that data into actionable intelligence that helps producers make better decisions.
We're looking for an AI Engineer to help build the next generation of intelligent capabilities within our products. You'll work at the intersection of data, machine learning, software engineering and product to turn complex, real-world data into production AI solutions.
This isn't about AI for AI's sake. Sometimes the right answer is machine learning. Sometimes it's optimization, classical statistics or generative AI. We want someone with the technical depth and judgment to know the difference.
What You'll Do- Build and deploy production-grade AI/ML solutions that turn complex farm data into actionable intelligence.
- Explore large, distributed and legacy datasets to uncover patterns and opportunities for new product capabilities.
- Engineer features, select modeling approaches and design experiments to evaluate outcomes.
- Move models from exploration into scalable, real-time production applications.
- Integrate, deploy, fine-tune and monitor models using cloud infrastructure, including AWS and Databricks.
- Build testing and observability for AI systems, including behavioral testing, trace analysis and agent debugging.
- Evaluate emerging technologies such as agentic AI, MCP and orchestration frameworks.
- Partner with product, engineering and dairy science teams to turn ambiguous customer problems into working solutions.
- Help advance the tools, standards and practices that will support AI development at VAS.
- Collaborate on user-facing AI experiences using React and TypeScript when needed.
What We're Looking ForWe're looking for someone who has gone deep at the intersection of data + machine learning and has a track record of putting models into production.
Your background might include software engineering, data engineering, data science, machine learning engineering or AI engineering. The path matters less than the depth of what you've built.
Ideally, you bring:
- Significant experience building production software and AI/ML solutions, with demonstrated depth in data, machine learning and model deployment.
- Experience working with complex, distributed or legacy data, including inconsistent schemas and incomplete records.
- Strong experience with feature engineering, model selection, experimentation and evaluation.
- The ability to work across approaches, from classical statistics and machine learning to modern generative AI, choosing the right solution for the problem.
- Experience taking models from experimentation through production deployment and monitoring.
- Experience with deep learning frameworks and cloud-based AI services.
- Experience with AWS and/or Databricks.
- Exposure to agentic architectures, MCP or modern AI orchestration frameworks.
- Strong software engineering fundamentals and a pragmatic approach to solving problems.
- The ability to translate ambiguous requirements into working systems and communicate technical tradeoffs across engineering, product and domain teams.
- A bachelor's degree in Software Engineering, Computer Science, Data Science, AI/ML or a related field is preferred.
You can build AI anywhere. Here, you can use it to help feed the world. 
