AI Automation Engineer

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Exempt Full-Time Professional Brea, CA, US

30+ days ago Requisition

Salary Range: $ To $ Annually

Monoprice runs a high-SKU direct-to-consumer e-commerce business on a proprietary platform with Microsoft 365 as our productivity backbone. We have AI workspace tools, Copilot, and Claude deployed across the team, with Claude desktop in active use among power users. The gap is not tooling. It is connecting those tools to the data and workflows that would make them genuinely useful for business teams.

This role sits at the center of our AI enablement program. The work is equal parts technical execution and human enablement. You will build data pipelines and automations that make our systems accessible to AI tools. You will train business teams to use what gets built. And you will document what you build so it compounds over time rather than creating a new dependency.

The forward-looking technical work here is extending AI tooling into internal systems via Python connectors and data pipelines. Open-platform automation experience is useful background. As the program matures, the work extends into AI-native tooling: connecting business users to live system data through direct queries and natural language. But the foundation is reliable automation and accessible data first.

This role does not have a defined team under it. You may work alongside product management and change management resources, but you should expect to own the technical execution of the AI enablement program independently and to build the program's reach through training and documentation, not headcount.

Essential Functions and Responsibilities

• Build data pipelines that make source system data (SQL Server, M365) accessible to AI tools and business users. The direction is source systems out to accessible destinations:
  Postgres, CSV, or direct AI tool integration.
• Build Python connectors and API integrations that extend AI tooling into internal data sources and systems. MCP server configuration is a growth area as the program scales, not a day-one requirement.
• Understand the data structure of our source systems well enough to scope what is buildable before committing to a solution. SQL Server is the source. It is not interchangeable with downstream destinations.
• Evaluate and use data integration tooling (Airbyte or equivalent) where appropriate. Know when a Python script or direct connector is the simpler answer.

• Build and deploy workflow automations using Microsoft Power Automate, Copilot Studio, and open-platform tools where they fit the problem. Prefer the simplest tool that solves the problem reliably.
• Own the full lifecycle: discovery, build, deployment, adoption, documentation. An automation nobody uses or nobody can maintain is not a completed project.
• Maintain a prioritized automation and data pipeline backlog. Communicate progress and blockers to leadership and department heads.

• Conduct workflow and data discovery sessions with non-technical business teams. The job in these sessions is to understand the problem and the underlying data before proposing any solution.
• Scope requirements to the minimum viable solution. Not every use case needs to be automated. Not every edge case needs to be handled in version one.
• Know when to tell a business user that an existing AI tool or automation already solves their problem if connected to the right data. Building something new is not always the answer.

• Train business teams on AI tools and automations as they are…