AI Automation Engineer

Overview

MONUMENT IS BUILT ON A COMMITMENT TO OUR PEOPLE AND OUR SHARED VALUES. THROUGH OUR GENUINE PASSION FOR DELIVERY , RELENTLESS PURSUIT OF PERFECTION , AND BOLD DECISION MAKING, WE ARE CREATING A MORE AGILE CONTINGENT WORKFORCE SO BUSINESSES CAN DREAM BIGGER AND ACHIEVE MORE . WE ARE MONUMENT Our passion is helping clients gain full transparency and control of their contingent workforce while achieving best-in-class performance in cost, quality, and compliance.

Best of all, we make it easy. Founded in 2003 with a sole focus on Contingent Labor programs and consulting services Consultants at our Core – Monument maintains a highly analytical consulting services team which provides a strategic approach unmatched by peer MSPs Global MSP with an ecosystem of partners spanning 50 countries and all major regions Vendor neutral model relies upon a trusted partner and supplier community Partners with industry-leading VMS systems More “Gen 1” implementations than any other MSP over the last 5 years.

Seamless extension of client HR, sourcing, business units, vendors and contractors OUR SHARED VALUES EVERYONE MATTERS WE TAKE CARE OF EACH OTHER LEADERSHIP IS HERE TO SERVE HIGH-CHARACTER AND HARD WORK ABOVE ALL ELSE ALWAYS KNOW WHERE YOU STAND EXCELLENCE ISN’T THE EXPECTATION, IT’S THE MONUMENT STANDARD Position Title: AI Automation Engineer Position Summary: We are looking for an AI Automation Engineer to build the agents and automations that Monument's own teams run on.

You will sit with the people who do the work in operations, finance, HR, and sales support, watch how the work actually gets done, and replace the manual parts with something that runs itself. This is an internal role. You are not billed to a client, and you do not carry an external account. Your stakeholders are the function owners inside Monument, and they are demanding in a different way than a client is: they sit next to you, they will tell you immediately when something does not work, and they have to live with what you build every day.

You build primarily on Microsoft: Copilot Studio, Power Automate, Power Apps, Dataverse, and the Microsoft 365 estate our teams already work inside. That is the default because it is where our data and our users already are, not because it is the only tool you are allowed to touch. When a problem is better solved another way, say so.

The measure of this job is use, not delivery. An agent nobody opens is a failure even if it works. You are expected to watch what happens after launch, fix what people actually trip on, and retire the things that did not earn their place. How the Work Runs Most of what you build will follow the same four steps. The first one is the one people skip, and it is the one that decides whether the rest was worth doing.

Steps: Sit with it: Watch the process being done by the person who does it. Count the clicks, the copy-pastes, the waiting, the rework. Do not work from a written process doc alone. Map and decide: Write down the process as it truly runs, including the exceptions. Then decide what to automate, what to simplify first, and what to stop doing altogether.

Build the thin slice: Ship the smallest working version to real users in days, not months. Get it into the tools they already have open, such as Teams, Outlook and SharePoint, rather than a new place to go. Instrument and hand over: Add logging and usage tracking. Write the runbook. Train the owner. Make sure it survives without you in the room.

Responsibilities

Process Discovery Shadow internal teams to understand how a process genuinely runs, including the workarounds nobody documented Produce a clear current-state map: steps, handoffs, systems touched, time spent, and where the errors happen Size the opportunity honestly, in hours returned, errors avoided, or cycle time cut, before anything gets built Push back when the answer is not automation: some processes should be simplified, consolidated, or deleted first Build & Configure Build conversational agents in Copilot Studio: topics, knowledge sources, actions, and escalation paths Build automations in Power Automate and applications in Power Apps, with Dataverse or SharePoint as the data layer Integrate with the systems our teams already use, including Microsoft 365, Teams, SharePoint and internal line-of-business tools, using connectors, APIs, or custom connectors where needed Write the code the low-code platform cannot handle: custom connectors, Azure Functions, scripts, and API integration work Apply the patterns, naming conventions, and environment standards set by the AI Solution Architect Test against real cases including the ugly ones: malformed inputs, exceptions, and the edge cases the process owner forgot to mention Adoption & Support Train the people who will use what you built, and write documentation they can actually follow Track usage after launch and act on what it tells you: where people abandon the flow, what they ask the agent that it cannot answer Own the fixes on what you have shipped.

You are not done at handover Report on what each automation returned, in hours or errors, not in features delivered Standards & Reuse Build to be reused: components, connectors, and prompt patterns that the next automation can pick up Contribute what you learn back to the internal pattern library so the same problem is not solved twice Flag governance and data-handling concerns early: who can see what, and what should never reach a model Help evaluate new platform capabilities as Microsoft ships them, and say plainly whether they are worth adopting The Stack: Microsoft-first, because that is where our users and our data already live.

Depth in the agent and automation layers matters most; the rest you can learn here. Agent: Microsoft Copilot Studio: topics, generative answers, actions, knowledge sources, agent flows Automation: Power Automate (cloud and desktop flows), Power Apps, Power Platform environments and ALM Data: Dataverse, SharePoint, Microsoft Graph, SQL, and the connectors between them Intelligence: Azure AI Foundry, Azure OpenAI, prompt design, retrieval over internal content, evaluation of outputs Code: C# or Python, REST APIs, custom connectors, Azure Functions, source control and deployment Governance: Power Platform admin, DLP policies, environment strategy, Microsoft Purview, role-based access Qualifications Required 3-5 years building business applications, integrations, or automations that real people use daily Hands-on delivery experience with Microsoft Power Platform, covering Power Automate and Power Apps at minimum Working knowledge of Copilot Studio or a comparable conversational agent platform Able to write real code when the platform runs out of road: C#, Python, or JavaScript, plus REST API integration Demonstrated ability to map a business process and identify what is worth automating Comfortable working directly with non-technical stakeholders and translating what they say into what they need Clear written communication: documentation, runbooks, and training material people can use Strongly Preferred Microsoft certifications: PL-200, PL-400, PL-600, or AI-102 Experience with Azure AI Foundry, Azure OpenAI, or retrieval-augmented generation over internal content Dataverse data modelling and Power Platform ALM across environments Robotic process automation background: Power Automate Desktop, Ui.

Path, or Automation Anywhere Experience automating back-office functions such as HR, finance, recruiting, or operations Exposure to Power Platform governance: DLP policies, environment strategy, and admin tooling Prior work inside a professional services or consulting firm Core Competencies Process Curiosity Build Velocity Platform Depth Stakeholder Partnership Adoption Focus Pragmatism Documentation Discipline Delivery Accountability Pay Range USD $110,000.

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• USD $135,000.00 /Yr. Process Discovery Shadow internal teams to understand how a process genuinely runs, including the workarounds nobody documented Produce a clear current-state map: steps, handoffs, systems touched, time spent, and where the errors happen Size the opportunity honestly, in hours returned, errors avoided, or cycle time cut, before anything gets built Push back when the answer is not automation: some processes should be simplified, consolidated, or deleted first Build & Configure Build conversational agents in Copilot Studio: topics, knowledge sources, actions, and escalation paths Build automations in Power Automate and applications in Power Apps, with Dataverse or SharePoint as the data layer Integrate with the systems our teams already use, including Microsoft 365, Teams, SharePoint and internal line-of-business tools, using connectors, APIs, or custom connectors where needed Write the code the low-code platform cannot handle: custom connectors, Azure Functions, scripts, and API integration work Apply the patterns, naming conventions, and environment standards set by the AI Solution Architect Test against real cases including the ugly ones: malformed inputs, exceptions, and the edge cases the process owner forgot to mention Adoption & Support Train the people who will use what you built, and write documentation they can actually follow Track usage after launch and act on what it tells you: where people abandon the flow, what they ask the agent that it cannot answer Own the fixes on what you have shipped. You are not done at handover Report on what each automation returned, in hours or errors, not in features delivered Standards & Reuse Build to be reused: components, connectors, and prompt patterns that the next automation can pick up Contribute what you learn back to the internal pattern library so the same problem is not solved twice Flag governance and data-handling concerns early: who can see what, and what should never reach a model Help evaluate new platform capabilities as Microsoft ships them, and say plainly whether they are worth adopting The Stack: Microsoft-first, because that is where our users and our data already live. Depth in the agent and automation layers matters most; the rest you can learn here. Agent: Microsoft Copilot Studio: topics, generative answers, actions, knowledge sources, agent flows Automation: Power Automate (cloud and desktop flows), Power Apps, Power Platform environments and ALM Data: Dataverse, SharePoint, Microsoft Graph, SQL, and the connectors between them Intelligence: Azure AI Foundry, Azure OpenAI, prompt design, retrieval over internal content, evaluation of outputs Code: C# or Python, REST APIs, custom connectors, Azure Functions, source control and deployment Governance: Power Platform admin, DLP policies, environment strategy, Microsoft Purview, role-based access Required 3-5 years building business applications, integrations, or automations that real people use daily Hands-on delivery experience with Microsoft Power Platform, covering Power Automate and Power Apps at minimum Working knowledge of Copilot Studio or a comparable conversational agent platform Able to write real code when the platform runs out of road: C#, Python, or JavaScript, plus REST API integration Demonstrated ability to map a business process and identify what is worth automating Comfortable working directly with non-technical stakeholders and translating what they say into what they need Clear written communication: documentation, runbooks, and training material people can use Strongly Preferred Microsoft certifications: PL-200, PL-400, PL-600, or AI-102 Experience with Azure AI Foundry, Azure OpenAI, or retrieval-augmented generation over internal content Dataverse data modelling and Power Platform ALM across environments Robotic process automation background: Power Automate Desktop, Ui. Path, or Automation Anywhere Experience automating back-office functions such as HR, finance, recruiting, or operations Exposure to Power Platform governance: DLP policies, environment strategy, and admin tooling Prior work inside a professional services or consulting firm Core Competencies Process Curiosity Build Velocity Platform Depth Stakeholder Partnership Adoption Focus Pragmatism Documentation Discipline Delivery Accountability