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# AI Developer

- **Company:** MatchPoint Solutions
- **Location:** Maryland Heights, MO, United States (Remote available)
- **Salary:** $83,200.0 - $93,600.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, C Sharp (Programming Language), Data Hub, Data Security, Information Technology Operations, JSON, Python (Programming Language), Azure Active Directory, Salesforce.Com, Microsoft SharePoint, YAML, Enterprise Data Management, Microsoft Power Automate, Power Platform Integration, Agentic-AI, Microsoft Onedrive, Microsoft Copilot Studio, Automation Anywhere, Powerapps, Human in the Loop, Servicenow
- **Published:** October 31, 2026
- **Apply:** https://www.careerjet.com/jobad/use1170312b599c7cfbfd4404971fcd416

## About the Role

Deep expertise across the Microsoft Power Platform (Power Automate, Power Apps, Copilot Studio) and Microsoft Graph API, with a clear understanding of delegated vs. application permissions and their security, consent, and operational implications.
Hands-on experience working with meeting transcripts, chat data, email, and enterprise metadata, including building custom connectors and designing appropriate authentication models, scopes, and consent flows.
Proven experience building complex AI agents in Copilot Studio (not just prompt authoring); understands token economics, context limits, and cost-amplification risks, and has intentionally constrained AI behavior to reduce cost, risk, and unintended outcomes.
Operates with a privacy-first mindset, assuming every data path is a liability until proven otherwise and consistently designing for minimum necessary access rather than maximum convenience.
Strong awareness of platform limitations-knows what Microsoft Graph, Copilot Studio, and the Power Platform cannot do and designs around those constraints intentionally instead of discovering them in production.
Pragmatic, polyglot builder who writes whatever the platform requires (C#, Python, Power Fx, JSON, YAML) without relying on a dedicated development team to unblock progress.
Fluent in IT and identity infrastructure; capable of authoring permission requests, validating access, and navigating Azure AD / Entra ID as a peer to IT Operations rather than a dependent.
Operates beyond documentation by testing edge cases, validating assumptions, and documenting findings when official guidance ends.
Comfortable engaging architects and executives in the same conversation, translating technical constraints into clear business and risk tradeoffs.
Brings a strong builder mentality-cannot stop at theory and is driven to make solutions work in production.
Demonstrates antifragile curiosity, leaning into permissions, APIs, and constraints rather than avoiding them.
Acts as a force multiplier by enabling others and sharing solutions rather than hoarding knowledge.
Maintains a healthy skepticism of AI hype, clearly understanding where AI fails and designing systems that account for those limitations.
Comfortable pushing back when architectural, security, or cost decisions do not make sense.

## Description

We need a high leverage builder who can turn delivery and operational processes into AI enabled, scalable internal systems. This role sits between product management, delivery leadership, enterprise data, and AI tooling - designing solutions that are secure, cost aware, and production ready, not experimental demos.
Core Responsibilities
AI-enabled operations design
Design AI workflows for delivery, PMO, resourcing, reporting, and governance.
Focus on boring, high-friction internal processes (status reporting, intake, metadata correlation, risk surfacing).
Explicitly control AI scope, token usage, and cost profiles (e.g., "1anddone" passes vs. conversational loops).
Data access & integration
Build and/or define secure, delegated access to enterprise data.
Microsoft Graph (mail, chats, meetings, transcripts), SharePoint/OneDrive, Salesforce, ServiceNow, Dataverse.
Create custom connectors or lightweight services so AI tools can access data safely and consistently.
Package complexity behind simple actions (e.g., "Search", "GetMeetingTranscripts", "GetProjectRoles").
Optimize for reuse across multiple agents and teams.
Human-in-the-loop system design
Design workflows that assume AI is probabilistic, and humans review, override, and approve.
Use adaptive cards, approvals, and structured outputs rather than free-text magic.
MVP-to-production translation
Identify why most AI pilots die: licensing, permissions, performance, and governance.
Proactively design guardrails before scale becomes a problem.
Adoption & enablement
Ensure tools built are used-track adoption, gather feedback, and iterate.
Train and enable stakeholders and the organization to use AI-enabled workflows and communicate clearly to non-technical stakeholders what the tools do and don't do.