AI Engineer

Overview

AI Engineer - Hybrid

• AI Governance & Strategy:
  Design and implement an AI governance framework (policies, risk tiers, acceptable use, vendor evaluation). Define AI standards across the organization aligned to NIST AI RMF or equivalent. Establish data privacy, PII handling, and compliance guidelines for AI use cases. Own AI vendor selection, contracting support, and ongoing evaluation.
• Architecture & Engineering:
  Architect and build the agent orchestration layer using tools such as N8N, Lang Chain/Lang Graph, or similar. Integrate AI services with core platforms:
  Service Titan, Domo, Snowflake, and internal data sources. Build and maintain RAG pipelines and vector search infrastructure for internal knowledge use cases. Deploy and manage AI workloads using cloud infrastructure (Cloudflare, Railway, Azure, or equivalent). Manage Claude (Anthropic) as the primary LLM and evaluate additional model providers as needed.
• End-User AI Enablement:
  Define internal AI usage practices, prompt standards, and productivity playbooks for business users. Build internal tools and automations that deliver measurable value to field ops, finance, and service teams. Partner with brand GMs and department leads to identify and prioritize AI opportunities. Train and support end users to safely and effectively use AI tools.
• Data Warehouse & Reporting:
  Build and maintain data pipelines that feed AI models — including ingestion, transformation, and validation layers. Collaborate with the Data & BI Analyst on Snowflake schema design and dbt model development to ensure AI-ready data. Develop and maintain automated reporting and dashboards in Domo that surface AI performance metrics, model outputs, and business KPIs. Own the integration between AI outputs and the reporting layer — ensuring model-generated insights are accessible to business users.
  
  Define and enforce data quality standards upstream of AI systems; partner with IT and ops teams to resolve data gaps. Support ad hoc data analysis and reporting requests as a secondary function alongside core AI engineering work.
• Data & Tooling
  
  Collaboration:
  
  Partner with the Data & BI Analyst to align AI outputs with the data warehouse and reporting layer (Snowflake/Domo). Ensure AI pipelines have clean, governed data inputs — coordinate on dbt models, Fivetran connectors, or similar.
• First 90 Days:
  Deliver an AI governance framework and acceptable use policy. Audit current AI tool usage (licensed and shadow) across the org. Define the agent orchestration architecture and select the primary tooling stack. Ship at least one internal automation that demonstrates measurable ROI. Present a 12-month AI roadmap to IT leadership.

• Required:
  • 3+ years hands-on experience building and shipping AI/ML solutions in production.
  • Demonstrated experience with LLM APIs, prompt engineering, and agent frameworks.
  • Proven ability to build governance frameworks, not just features.
  • Experience integrating AI into operational workflows in non-tech industries (field services, healthcare, logistics, or similar).
  • Strong understanding of data pipelines and how AI systems consume structured/unstructured data.
  • Hands-on experience with a cloud data warehouse (Snowflake strongly preferred) and BI tooling (Domo, Tableau, Power BI, or similar).
  • Comfortable writing SQL and building data models; experience with dbt or similar transformation tools a plus.
  • Comfortable working in both a builder capacity and advising stakeholders at all levels.
  • Experience with cloud infrastructure for AI workload deployment.
• Preferred:
  • Experience with Service Titan or similar field service management platforms.
  • Familiarity with NIST AI RMF, ISO 42001, or comparable AI governance standards.
  • Prior experience in a multi-location, multi-brand, or franchise-model business.
  • Exposure to Model Ops tooling:
    Lang Smith, Helicone, Weights & Biases, or similar.
  • Experience with dbt, Fivetran, or Airbyte for data pipeline work.

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