AI Automation Engineer

Monoprice Inc.•Brea, CA

1d•$120,000 - $150,000

About The Position

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.

Requirements

Demonstrated hands-on experience delivering data pipeline and workflow automation solutions end-to-end in an enterprise environment, including deployment and adoption, not just build.
Ability to sit with a non-technical business team, understand their workflow and the data behind it, and scope what is buildable before proposing a solution.
SQL proficiency sufficient to understand source system data structures and write queries to extract and transform data for downstream use.
Experience connecting source databases (SQL Server or equivalent) to downstream destinations (Postgres, CSV, API endpoints) using integration tooling or custom connectors.
Microsoft 365 automation experience: Power Automate, Copilot Studio, SharePoint, Teams, Outlook.
Python or JavaScript for connectors, transformations, and API integrations.
Track record of automations and data pipelines that business teams actually use and can maintain.

Nice To Haves

Background in business process analysis, operational improvement, or process engineering that crossed into technical execution.
Experience training non-technical users on automation tools or workflows and driving real adoption.
Experience working without a dedicated data engineering team, where you had to figure out data access independently.
API connector development experience.
MCP server configuration is a plus but not a prerequisite; the right candidate will grow into it as the program matures.
Familiarity with the Claude API.
Prior work in e-commerce, retail, or a high-SKU catalog environment.

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.
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 deployed. Adoption is part of delivery. If the team cannot use it without you, the project is not done.
Document what gets built: what it connects to, what data it uses, how to maintain it, and how to extend it. The goal is compounding capability, not a new dependency.
Establish intake processes so business teams can request and prioritize AI enablement work without routing everything through you individually.