Senior AI/ML Engineer at URUS – United States | Jobyssey

Turn decades of data into intelligence that helps feed the world.

VAS is the Operating System of the modern dairy with decades of longitudinal data for the most productive cows in the world. We hold a dominant US market position, and an expanding global reach.

We are seeking a Senior AI/ML Engineer to lead the building of scalable real-time production-grade applications that use AI/ML models to drive actionable intelligence on dairy farms. This is a strategic, hands-on position for an experienced technical leader who has a track record of shipping AI-enhanced customer applications and tooling used by engineering teams.

Our highly customizable on-farm systems give dairy owners unmatched flexibility in how they run their business. The right candidate sees that as a data challenge, where others will see it as an unsolvable mess.

RESPONSIBILITIES

AI Enablement

Understand customer challenges and how integrating AI capabilities can help lead to solutions that have AI as a differentiator.
Identify opportunities to apply AI for efficiency, growth, and customer value
Drive awareness of AI capabilities and demonstrate how it can address customer needs, improve efficiency, reduce costs, and drive growth
Drive transformation from AI-Ad Hoc to AI-Native engineering practices
Serve as an AI technical SME, conduct R&D to meet the needs of our AI strategy
Continuously assess emerging AI tools and make data-driven recommendations
Measure & Accelerate Adoption: Establish KPIs, track progress from the current to 100% adoption, implement interventions to accelerate uptake and communicate impact
Build Center of Excellence: Create forums for knowledge sharing, celebrate wins, and foster peer-to-peer learning
Cross-functional communication, explaining technical tradeoffs to product, dairy science, and engineering leadership in plain language.
Working with other enterprise stakeholders, establish AI governance frameworks and guardrails covering compliance, security, privacy, and ethical AI practices, and embed them into development workflows

Core AI Engineering Skills

Comfort across the full method spectrum, from classical statistics and operations research through machine learning to modern generative AI, choosing the simplest tool that solves the problem.
Data-wrangling skill with messy, distributed, legacy enterprise data sources, including inconsistent schemas and incomplete records.
Feature-engineering and data preprocessing for both structured farm data and unstructured sources.
Model selection and evaluation, knowing when linear regression, optimization, or a lookup table beats a neural network.
Production deployment experience, shipping models into real time applications rather than notebooks.
Cloud AI infrastructure fluency, specifically Databricks and AWS.
Experiment design and statistical rigor, being able to prove a model or method actually improves outcomes.
Translating ambiguous business or technical requirements into working systems.
Agentic and MCP experience

Evaluation, Testing & Observability

Build unit and behavioral tests for agents, tools, and workflows.
Develop tooling for trace analysis, agent state debugging, and hallucination tracking.
Compare and benchmark agent orchestration frameworks for trade-offs in speed, reliability, and usability.

Model Fine-Tuning & MLOps

Integrate, deploy, fine tune and monitor models in production using cloud providers.
Set up agent logging, observability dashboards, and recovery workflows.

Front-end & User Experience

Collaborate with front-end developers or build user-facing components using React, TypeScript.
Ensure seamless user and agent interaction via UI and API bridges.

EDUCATION & EXPERIENCE

Your background might include software engineering, data engineering, data science, machine learning engineering or AI engineering. What matters most is demonstrated technical depth and a track record of building and deploying AI/ML solutions in production.

Significant hands-on experience designing, building and deploying production AI/ML solutions.
Strong experience working with complex data, including distributed systems, inconsistent schemas and incomplete or legacy datasets.
Experience with feature engineering, model selection, experimentation and evaluation.
Strong understanding of descriptive, predictive, prescriptive and generative AI approaches.
Experience selecting and applying techniques across statistics, operations research, machine learning and deep learning.
Demonstrated 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.
Experience or exposure to agentic architectures, MCP and AI orchestration frameworks.
Strong software engineering fundamentals and experience building scalable, production-quality systems.
Ability to translate ambiguous requirements into working solutions and clearly communicate technical decisions and tradeoffs.
Bachelor’s degree in Software Engineering, Computer Science, Data Science, AI/ML or a related field preferred.

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