Full Stack Engineer Ai
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Job Description
Applied AI (Microsoft Full Stack AI / FDE)
1. Role Overview
We are seeking a visionary and hands-on Microsoft AI Lead to spearhead our Applied AI capability within the Microsoft Full Stack AI (FDE) practice. This leader will own the end-to-end strategy, delivery, and growth of five interconnected AI pillars — M365 Copilot Agents, Microsoft Native AI Agent Orchestration, Microsoft Foundry, Power Platform AI, and Responsible AI Governance — driving measurable business outcomes for enterprise clients. The ideal candidate combines deep Microsoft AI platform expertise with the executive presence and commercial acumen to build a high-performing team and drive growth.
2. Key Responsibilities
M365 Copilot Agents + Deploy
- Lead design and deployment of Microsoft 365 Copilot agents (Declarative & Custom Engine Agents) across enterprise clients.
- Define best practices for Copilot extensibility using Microsoft 365 Agents SDK, Graph connectors, and Teams AI Library.
- Oversee adoption and change-management programmes to maximise Copilot ROI for clients.
Microsoft Native AI Agent Orchestration
- Architect multi-agent solutions leveraging Azure AI Agent Service, Semantic Kernel, and AutoGen frameworks.
- Drive development of orchestration patterns (sequential, parallel, handoff) for complex AI workflows.
- Ensure seamless integration between agents, enterprise data sources, and downstream systems of record.
Microsoft Foundry (Azure AI Foundry)
- Lead client engagements involving Azure AI Foundry for model fine-tuning, evaluation, and deployment at scale.
- Build accelerators and reusable assets on the Foundry platform to reduce time-to-value.
3. Required Skills & Experience
- Deep experience in technology consulting or enterprise software, with at least 5 years in AI/ML leadership roles.
- Deep hands-on expertise across the Microsoft AI stack: Azure OpenAI Service, Azure AI Foundry, Semantic Kernel, Copilot Studio, Power Platform, and Microsoft 365.
- Strong understanding of LLM architectures, RAG patterns, agentic workflows, and prompt engineering.
- Experience delivering complex AI transformation programmes for clients in regulated industries.
- Demonstrated ability to translate technical AI capabilities into compelling business value propositions.
- Excellent executive communication, client relationship management, and stakeholder influence skills.
- Strong commercial acumen — skills in deal structuring, SOW development, and contract negotiations.
4. Preferred Qualifications
- Microsoft certifications: Azure AI Engineer Associate (AI-102), Azure Data Scientist (DP-100), Power Platform Solution Architect (PL-600).
- Experience as a Microsoft Partner-side architect or having held roles within Microsoft (FTE/FDE/CSA).
- Familiarity with MLOps pipelines, Azure Machine Learning, and model governance tooling.
- Contributions to open-source AI projects or publications in AI/ML domains.
- Experience with multi-cloud AI strategies and interoperability with AWS Bedrock or Google Vertex AI.