Notice Period: Apply only those who are available to join within 15 days or less.
Job Description
Title: AI Field Deployment Engineer- Business Analyst
Department: Business Transformation, AI Adoption & Value Realization
Position Summary
Bridges business operations, AI technology, and enterprise transformation. Works directly with stakeholders to redesign processes and implement AI-powered solutions across Microsoft 365 Copilot, Copilot Studio, GitHub Copilot (incl. Coding Agents), Power Platform AI, Azure AI, and enterprise AI agents.
Key Responsibilities
- Business Discovery & Analysis — stakeholder interviews, process mapping, business cases, requirements documentation, AI transformation roadmaps
- AI Opportunity Identification — assess applicability, evaluate Copilot/GitHub Copilot use cases, estimate ROI, prioritize backlog
- Solution Design — future-state processes, user journeys, functional specs, user stories, governance alignment
- Solution Delivery, Deployment & Configuration
- A. Direct Configuration: hands-on setup of standard/low-code AI tools, prompt libraries, workflow automation, production rollout
- B. Specification & Delivery Management: functional leadership for custom/advanced AI builds, UAT coordination, cross-team collaboration with engineers/architects
- Adoption & Change Management — training, enablement materials, adoption monitoring, barrier removal
- Value Realization & Continuous Improvement — KPI frameworks, ROI assessments, executive reporting, scaling roadmaps
Required Competencies
- Business Analysis & Consulting: process reengineering, requirements elicitation, workshop facilitation, business case development, change management
- AI & Technology: Generative/Agentic AI, Microsoft Copilot ecosystem, Power Platform, Azure AI Foundry, prompt engineering, Responsible AI/governance
- Delivery & Transformation: Agile, functional spec writing, testing/validation, benefits realization, vendor coordination
- Soft Skills: executive communication, storytelling, influence without authority, facilitation
Success Metrics (KPIs)
Use cases deployed, adoption rate, active user growth, productivity/hours saved, time-to-value, ROI, stakeholder satisfaction, developer productivity gains, solutions scaled across functions, governance compliance.
Role Summary
A business-first AI professional accountable end-to-end: hands-on delivery for standard platforms, and business ownership/governance for advanced custom AI builds executed by specialized engineering teams.
