Note: The job is a remote job and is open to candidates in USA. VAS, an operating system for modern dairy operations, uses decades of farm and livestock data to provide actionable intelligence to dairy producers. The Senior AI/ML Engineer will lead the development and deployment of scalable, real-time AI/ML applications, establish AI engineering practices and governance, and support model evaluation, MLOps, agentic systems, and user-facing AI experiences.
Responsibilities
- 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
- 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
- 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
- Integrate, deploy, fine tune and monitor models in production using cloud providers
- Set up agent logging, observability dashboards, and recovery workflows
- Collaborate with front-end developers or build user-facing components using React, TypeScript
- Ensure seamless user and agent interaction via UI and API bridges
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
- 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
Benefits
- Full-time employment
- Remote work arrangement
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