AI Engineer

VAS Dairy Management software•Watertown, WI

About The Position

VAS is seeking an AI Engineer to lead building of scalable real-time production grade applications that use AI/ML models. 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. This role will be focused on embedding LLMs, agent-based systems, and automation into core development workflows—boosting productivity, reducing manual toil, and accelerating delivery, essentially transforming how our engineers build, test, and ship software.

Requirements

Bachelor of Science in Software Engineering, Computer Science, Data Science, AI/ML or related field preferred.
10+ years of experience in software development and design.
Proven experience in AI/ML solution design and hands experience with AI-powered development tools. 3+ years of experience preferred.
Strong knowledge of Large Language Models, Generative AI, NLP, and Machine Learning concepts. (3+ years of experience preferred).
Hands-on experience with deep learning frameworks.
Experience with RAG pipelines, vector databases, and agentic frameworks.
Familiarity with cloud-based AI services.

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 the AI technical SME, conduct R&D (research and development) 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.
Establish AI governance frameworks and guardrails covering compliance, security, privacy, and ethical AI practices, and embed them into development workflows.
LLM Agents & Prompt Engineering: Architect and implement LLM agents. Build composable, tool-augmented reasoning chains (e.g., RAG, CoT, ReAct, planner-executor). Integrate vector databases and knowledge graphs to support retrieval-augmented generation (RAG). Design and maintain high-quality prompt strategies for robustness and reliability.
Model Context Protocol (MCP) & Backend: Develop and maintain scalable APIs, supporting synchronous and asynchronous agent execution. Integrate Model Context Protocol (MCP) to enable secure and structured access to external data and tools within agent workflows. Implement state tracking, context-aware input dispatch, and modular plugin integration within the control plane.
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.