AI Platform Engineer

Position Summary:

Vallen Distribution is building a governed, production-grade AI capability on an Azure-first, Databricks-centered architecture. This is not an advisory role — it is a hands-on builder position with broad ownership across the AI layer.

The AI Platform Engineer will own the Databricks AI/ML platform layer, drive enterprise AI governance, review and remediate shadow AI solutions, and directly deliver automation use cases with business teams. You will be the internal AI expert for the organization — the first call for departments exploring automation and the technical authority on what gets built, how, and where data flows.

This is a greenfield opportunity with real scope and visibility. You'll work directly with the SVP of Data & Technology Innovation and have immediate impact on a program that is active and growing today.

Essential Duties and Responsibilities:

Lakehouse AI Layer (Databricks) — ~35%

• Own the AI/ML layer on Databricks: feature stores, MLflow experiment tracking and model registry, and RAG/prompt architectural standards
• Define and enforce prompt engineering standards and LLM integration patterns across internal tools
• Partner with Data Engineering to design data pipelines that feed AI/ML use cases from the Unity Catalog lakehouse
• Evaluate and implement agentic frameworks (Claude API, Databricks AI agents) for internal automation

Automation Delivery — ~25%

• Directly build and deliver 2–3 automation use cases per year with business teams (HR, Legal, customer service, operations)
• Own full delivery lifecycle: scoping, design, build, testing, and handoff to platform operations
• Produce well-documented, governed solutions — not one-off scripts

AI Governance & Shadow Solution Review — ~20%

• Serve as the first-filter reviewer for all AI tools and platforms proposed for use at Vallen
• Partner with Security and Infrastructure to assess data classification risk, vendor posture, and integration risk before production deployment
• Review user-built solutions (Claude Desktop, Copilot, Cowork, and similar tools) and determine when a productivity workflow has crossed into enterprise scope — then lead the governed rebuild
• Maintain Vallen's enterprise AI acceptable use policy, data classification guardrails, and platform-tier standards

Use Case Evangelism & Business Partnering — ~20%

• Embed with departments to surface and prioritize automation opportunities
• Maintain a scored use case backlog; facilitate structured discovery sessions with business stakeholders
• Serve as the internal AI resource teams engage before going external