Sr Engineer, AI Implementation
New
Based in the United StatesFull-TimeSenior
Salary$115,000 to $161,000
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Job Details
- Experience
- 5–7+ years of professional experience
- Required Skills
- SharePointCI/CDGitHubGitLab
Requirements
- 5–7+ years of professional experience in software engineering, AI engineering, automation, or related technical disciplines.
- 2–3+ years of hands-on experience designing, developing, and deploying AI solutions, workflow automation, or Microsoft 365 Copilot implementations.
- Strong expertise with Microsoft 365 Copilot, Copilot Studio, Microsoft Graph, SharePoint, Power Platform, Azure AI services, and enterprise AI technologies.
- Experience implementing AI governance, content management, Microsoft Purview, security controls, semantic indexing, and enterprise data protection practices.
- Solid understanding of CI/CD pipelines, GitHub or GitLab, Infrastructure as Code, monitoring, observability, troubleshooting, and production support.
- Proven ability to translate business requirements into practical AI solutions that deliver measurable value and user adoption.
- Excellent communication, collaboration, coaching, and documentation skills, with experience enabling both technical and non-technical teams.
- A pragmatic, solution-oriented mindset focused on scalability, governance, operational excellence, reliability, and continuous improvement.
Responsibilities
- Partner with business leaders to identify high-value opportunities where AI agents and Copilot solutions can improve productivity, decision-making, and operational efficiency.
- Design, build, deploy, and optimize AI agents using Microsoft 365 Copilot, Copilot Studio, Microsoft 365 Agents SDK, Power Automate, and related Microsoft AI technologies.
- Integrate AI solutions with Microsoft Teams, Outlook, SharePoint, Word, Excel, Microsoft Graph, OneDrive, and other enterprise data sources.
- Develop production-ready AI implementations through strong version control, deployment pipelines, monitoring, observability, and incident management practices.
- Establish governance standards covering content architecture, grounding sources, security, access controls, metadata, prompt management, and AI lifecycle management.
- Create reusable templates, reference architectures, documentation, workshops, and training programs that empower business teams to build and manage their own AI solutions.
- Collaborate closely with IT, Security, Compliance, and AI leadership teams to ensure enterprise standards for governance, privacy, scalability, and operational excellence.
- Continuously improve AI adoption, reliability, cost efficiency, performance, and measurable business outcomes through experimentation, analytics, and ongoing optimization.
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