Job Code: JPC - 6104
Job Title:AI & Digital Experience Analyst
Job Location: Abu Dhabi
Contract: 12 Months
Job Summary
The AI & Digital Experience Analyst serves as the execution and innovation engine for the Technology Operations division. Reporting directly to the Associate Director of Technology Operations, this role focuses on translating high-level AI architectures into functional prototypes, driving enterprise-wide AI adoption, including Microsoft Copilot, and measuring the operational impact of the Technology Operations division.
The role acts as the in-house Copilot Expert, responsible for enabling and coaching end users to build their own agents in Microsoft Copilot Studio, including advanced, multi-step agents that connect to enterprise data and automate business workflows.
The Analyst bridges the gap between technical AI development and end-user experience, ensuring that Technology Services is positioned as a proactive, value-driving partner to the business.
Core Accountabilities
- AI Prototyping & Execution: Act as the primary builder for internal AI agents and automation tools, transforming architectural designs and logic workflows provided by TechOps leadership into testable, functional models.
- Copilot Studio Expertise: Serve as the subject-matter expert for Microsoft Copilot Studio, helping end users design, build, and publish their own agents, from simple question-and-answer bots to advanced agents with custom topics, actions, connectors, knowledge sources, and orchestration.
- Enterprise AI Adoption: Lead user campaigns and training programs across the organization to drive adoption of digital workplace tools, specifically spearheading Microsoft Copilot training initiatives.
- Strategic Impact Reporting: Design and maintain executive dashboards and Impact Stories that translate complex TechOps achievements, such as automated ticket deflection, infrastructure cost savings, and system uptime, into clear business-facing metrics.
Operational 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.
Job Qualifications & Experience
- 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.
