Job Description

Roles & Responsibilities

• 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.
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