Copilot Studio Enablement: Run hands-on workshops and one-to-one coaching sessions that guide end users through building agents in Microsoft Copilot Studio, including advanced capabilities such as custom topics, generative actions, knowledge sources, connectors, and agent orchestration.
Model Training & Testing: Ingest, format, and structure internal data, including policies and IT documentation, for AI knowledge bases. Conduct rigorous prompt testing, log hallucination rates, and refine agent outputs prior to production deployment.
Digital Experience Advocacy: Conduct live training sessions and workshops with business units to demonstrate the capabilities of deployed AI tools and gather user feedback for continuous improvement.
Proof of Concept (PoC) Development: Rapidly build internal PoCs using existing platforms to validate business requests before full implementation approval.
Governance & Best Practice: Define and share reusable templates, guardrails, and best-practice guidance so that user-built Copilot Studio agents remain secure, compliant, and aligned with organizational standards.
Performance Tracking: Monitor the daily usage and success rates of deployed AI Service Desk agents and provide weekly analytics on automated ticket resolution.
Desired Candidate Profile
5–10 years of overall experience.
Bachelor’s degree in Artificial Intelligence, Computer Science, Information Technology, or a related technical field.
Strong foundational understanding of Large Language Models (LLMs), prompt engineering, and AI toolsets.
Hands-on familiarity with Microsoft Copilot and Microsoft Copilot Studio, including building and configuring agents, topics, actions, and connectors.
Excellent communication and presentation skills, with the ability to explain technical concepts to non-technical business users.
Basic scripting capability, such as Python or PowerShell, for data formatting and API interactions.
High capacity for independent problem-solving and rapid prototyping.
Keep up to date with the latest AI tools and model releases and identify opportunities to leverage them internally.
High-agility, cloud-first infrastructure requiring rapid adaptation to new AI frameworks and continuous learning.