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This role requires strong engineering execution, systems thinking, sound judgment, clear communication, and the ability to operate across a complex enterprise environment with evolving AI technology, governance expectations, and stakeholder needs.

What You'll Do

• AI Solution Engineering and Agent Platform Development.Design, build, test, deploy, and support AI-enabled solutions using Microsoft 365 Copilot, Copilot Studio, Power Platform, Azure AI services, Azure OpenAI, Microsoft Graph, approved connectors, APIs, and related enterprise AI technologies. Develop patterns for retrieval-augmented generation, grounding, agent instructions, prompt libraries, workflow automation, human-in-the-loop review, and safe action execution.

• M365 Copilot, Copilot Studio, and Agent Governance.Define and operate engineering standards for enterprise, team, and individual agents, including ownership, access, sharing, environment strategy, lifecycle, monitoring, retirement, and support expectations. Partner with AI Program, Enterprise Architecture, Information Security, Enterprise Data, Legal, Privacy, Records, and platform owners to translate enterprise AI policy into platform-level guardrails and repeatable implementation patterns.

• Power Platform and Low-Code AI Engineering.Establish technical patterns for Copilot Studio, Power Apps, Power Automate, Dataverse, connectors, solution packaging, environment management, application lifecycle management, deployment pipelines, connection references, and reusable components. Help mature Power Platform governance into a scalable model for agents, apps, flows, and citizen-developer solutions.

• M365 Data Readiness for AI.Improve the quality, security, and usefulness of Microsoft 365 data for AI experiences by partnering on SharePoint, OneDrive, Teams, and Exchange governance. Support work that strengthens permissions hygiene, reduces oversharing, improves search and discoverability, improves knowledge-source curation, and aligns retention, sensitivity, and information architecture practices with AI usage.

• AI Platform Operations, Monitoring, and Cost Management.Build operational practices for AI-enabled platforms, including usage reporting, cost and consumption monitoring, auditability, incident response, service health, platform baselines, risk reviews, and continuous improvement. Help leadership understand adoption, value, cost drivers, control gaps, and platform risks across Copilot, Copilot Studio, Power Platform, Azure AI, and other approved enterprise AI tools.

• Architecture, Security, and Responsible AI Controls.Design secure data access and integration patterns using least privilege, scoped connectors, existing user permissions, enterprise identity, audit logging, DLP alignment, and approved architecture standards. Evaluate AI solutions for accuracy, retrieval quality, grounding quality, prompt safety, latency, cost, permissions behavior, and operational supportability.

• Cross-Functional Delivery and Technical Leadership.Partner across the IT department and foundation wide, with TPMs, engineers, architects, service owners, vendors, and business stakeholders to move high-value AI scenarios from concept to sustainable production support. Create technical documentation, runbooks, decision records, platform patterns, and executive-ready materials that explain recommendations, tradeoffs, risks, and required decisions clearly.

• Continuous Improvement and Emerging Technology Evaluation.Stay current on Microsoft 365 Copilot, Copilot Studio, Power Platform, Azure AI, agentic AI, enterprise AI governance, and related industry trends. Evaluate new capabilities pragmatically, recommend where they fit, and help the foundation adopt new AI technologies with the right balance of pace, value, security, and operational discipline.

Your Experience

• 10+ years of relevant experience in AI engineering, software engineering, cloud engineering, platform engineering, Microsoft 365 engineering, Power Platform engineering, or equivalent practical experience.

• Hands-on experience building, integrating, or operating enterprise AI solutions using large language models, retrieval-augmented generation, semantic search, AI agents, prompt engineering, workflow orchestration, or AI-enabled automation.

• Experience with Microsoft 365 Copilot, Copilot Studio, Power Platform, Azure AI services, Azure OpenAI, Microsoft Graph, SharePoint Online, Teams, OneDrive, Entra ID, PowerShell, APIs, or related Microsoft cloud technologies.

• Experience designing secure data access patterns for AI-enabled solutions, including permissions inheritance, least privilege, scoped connectors, identity management, auditability, DLP alignment, and information protection controls.

• Experience with Power Platform governance and ALM concepts, including environments, connectors, managed solutions, pipelines, Dataverse, service accounts, connection references, and citizen-developer support models.

• Experience improving M365 data readiness for AI, including permissions cleanup, content lifecycle, search and discoverability, knowledge-source curation, information architecture, retention, sensitivity labels, or data governance practices.

• Experience with AI evaluation and operational practices such as response quality testing, retrieval testing, prompt testing, hallucination reduction, monitoring, observability, feedback loops, latency, and cost optimization.

• Strong engineering judgment and documentation skills, including the ability to create reusable patterns, technical standards, architecture decision records, runbooks, and support procedures.

• Ability to translate business needs and governance requirements into secure, scalable, supportable technical solutions.

• Strong communication skills, including the ability to explain technical concepts, platform constraints, risks, and recommendations to technical and non-technical audiences.

• Ability to navigate ambiguity, influence without authority, build trust across diverse stakeholder groups, and drive clarity in a fast-moving AI technology environment.

• Experience working across Information Security, Legal, Privacy, Records, Enterprise Architecture, data teams, engineering teams, vendors, and business stakeholders is preferred.

• Preferred experience includes Microsoft certifications in Azure, Microsoft 365, Power Platform, Security, AI, or Architecture; experience in regulated or complex enterprise environments; and experience with ChatGPT Enterprise, Claude Enterprise, Semantic Kernel, Azure AI Search, or similar agent and orchestration technologies.

First-Year Success Looks Like

• AI engineering operating model is defined and adopted, with clear paths for self-service, Interact consulting, complex engineering builds, production support, and escalation.

• Reusable engineering patterns, templates, and reference architectures are in place for Microsoft 365 Copilot, Copilot Studio, Power Platform, Azure AI, connectors, grounding, evaluation, and secure agent lifecycle management.

• Agent and automation governance is operationalized from intake through retirement, including ownership, review criteria, environment strategy, sharing controls, monitoring, support expectations, and risk-based escalation.

• M365 data readiness priorities are sequenced with TPM, engineering, Information Security, Legal, Privacy, Records, Enterprise Data and business partners, with practical improvements underway for permissions hygiene, content quality, discoverability, and oversharing risk.

• A small set of high-value AI-enabled productivity scenarios has moved from concept to sustainable production support, with documented value, adoption plan, support model, and clear ownership after launch.

• Usage, adoption, cost, risk, and platform-health reporting is available to inform leadership decisions, prioritize demand, and show where AI is creating measurable business value.

• Runbooks and escalation paths are documented and exercised for AI-related incidents, unsafe or low-quality outputs, access issues, connector failures, new feature releases, and platform service changes.

• The broader Productivity & Collaboration team is stronger because reusable documentation, demos, code patterns, decision records, and knowledge transfer make future AI work less bespoke and more durable.

*Must have unrestricted work authorization in the country where this position is located.