AI Platform Engineer

Why Join Our Team?

At Vallen, we embrace what makes us unique. We thrive on the diversity of our associates and the different ways each of us contributes to Vallen’s success. We pride ourselves on being an inclusive company that values the growth of our associates.

Benefits

• Vallen Medical, Dental, and Vision Insurance
• Medical, Dental, and Vision Insurance for Single, Employee + Spouse or Children, and Family Coverage
• 401(k) with discretionary company match of $0.50 on the $1.00 up to 6% of pay (U.S. positions only)
• Employer-paid Basic Life Insurance for Employee, Spouse, and Dependents
• Employer-paid Short-Term and Long-Term Disability Benefits and Parental Leave (and any leave required under federal, state, and local laws)
• Health Care and Dependent Care Flexible Spending Accounts
• Paid Time Off (Vacation and Sick Days): 80–160 hours of vacation time based on seniority, accrued monthly and prorated from date of hire. 40–56 hours of sick time per year based on seniority and advanced upon hire.
• Paid Time Off (Holidays): 8 scheduled holidays plus 2 floating holidays
• Employee Assistance Program
• Employee Resource Groups for networking and team building
• Tuition Reimbursement Program
• Employee Referral Program
• Safety shoe and safety glasses reimbursement (based on position)
• Employee discounts through BenefitHub
• Advancement opportunities
• Vallen complies with all minimum wage laws

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.

Core 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

Job Qualifications

• 2–4 years of hands-on experience in AI/ML engineering, data engineering, or a closely related technical role
• Hands-on experience with Databricks — MLflow, notebooks, Unity Catalog, and Python/SQL workflows; or equivalent lakehouse platform experience with demonstrated ability to ramp quickly
• Practical experience building with LLMs: prompt engineering, RAG pipelines, or API integration— production or project-level experience counts
• Strong Python skills; ability to own full solution delivery from prototype to production
• Solid grounding in Azure: Azure OpenAI, Azure Data Lake Storage, Azure Key Vault, and core networking/security concepts
• Demonstrated ability to work across technical and non-technical stakeholders — you can explain what you're building and why it matters
• Experience reviewing or documenting AI/ML solutions for risk, data sensitivity, or governance considerations

Preferred Qualifications

• Experience in distribution, supply chain, or industrial B2B environments
• Familiarity with agentic frameworks: Databricks AI Agent Framework, LangChain, AutoGen
• Familiarity with enterprise AI governance concepts: acceptable use policies, data classification tiers, model risk review
• Experience with Azure DevOps (ADO), CI/CD pipelines
• Familiarity with Power BI, Databricks Genie, or other BI/AI consumption layers
• Working knowledge of MDM, ERP data structures, or multi-system data environments