Program Management & Delivery
- Lead end-to-end delivery of Agentic AI projects, from ideation and PoC to deployment and scaling across Microsoft platforms.
- Manage project scope, timelines, budgets, and risks while ensuring alignment with business goals and technical feasibility
Microsoft AI Ecosystem Integration
- Coordinate the implementation of Microsoft-native AI components including Azure AI Foundry, Power Automate, and Copilot integrations.
- Ensure solutions are compliant with Microsoft’s Responsible AI principles, including cost optimization, bias detection, and audit workflows
Stakeholder Engagement
- Act as the primary liaison between business stakeholders, technical teams, and Microsoft partners.
- Facilitate workshops, demos, and governance reviews to ensure stakeholder alignment and transparency
Agile Execution & Governance
- Apply Agile/Scrum methodologies to manage sprints, backlogs, and iterative delivery cycles.
- Track KPIs, manage change requests, and ensure documentation and audit readiness.
Innovation & Reusability
- Promote reuse of accelerators, templates, and frameworks across AI projects.
- Contribute to the development of delivery playbooks and best practices for Microsoft Agentic AI programs
Required Skills & Qualifications
- Bachelor's / Masters degree in computer science, Engineering, Mathematics, Statistics, or related field
- Proven experience managing AI/ML or GenAI projects, ideally within Microsoft environments.
- Strong understanding of Microsoft AI stack: Azure AI, Power Platform, Microsoft Copilot, and Semantic Kernel.
- Familiarity with orchestration frameworks like LangChain, LangGraph, or AutoGen is a plus.
- Experience in managing deploying activities with Gen AI stack/services provided by various platforms such as AWS, GCP, Azure
- Experience in project management for AI/ML, Generative AI and Agentic AI Projects.
- Excellent communication, stakeholder management, and cross-functional leadership skills.
- PMP, PRINCE2, or Agile certifications preferred.
- Collaborate with data engineers, data scientists, and business stakeholders to understand the data and the business problems.