Senior AI Architect, Global Black Belt

Leading AI and machine learning solutions for global enterprises, improving operational efficiency and driving AI adoption across multiple industries.

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Executive Summary

As a Senior Generative AI Specialist within Microsoft’s Global Black Belt (GBB) organization, I have led enterprise-wide Generative AI adoption, driving business transformation across multiple industries. I have successfully led 100+ enterprise AI engagements with customers representing over $500M in Annual Recurring Revenue (ARR). My role sits at the intersection of AI strategy, enterprise architecture, and customer success—partnering directly with CXOs and business leaders to translate AI vision into scalable, production-grade solutions.

Company Overview

Microsoft is a global technology leader offering cutting-edge cloud, AI, and enterprise solutions. Through Azure AI, Microsoft enables organizations to transform their operations, create new value streams, and responsibly adopt Generative AI at scale.

My Mission at Microsoft

  • Drive enterprise Generative AI adoption and cloud transformation initiatives.
  • Translate complex business problems into scalable AI-first solutions using Azure OpenAI, Cognitive Services, and AI accelerators.
  • Accelerate time-to-value for customers by providing ready-to-deploy AI accelerators and reusable architectures.
  • Lead multi-disciplinary teams across technical, business, and executive domains to ensure AI solutions deliver tangible business impact.

Core Responsibilities

  • Enterprise AI Leadership: Define, lead, and execute AI transformation strategies for top-tier enterprise customers.
  • Generative AI Architecture: Design end-to-end AI solutions leveraging Azure OpenAI, AI Search, Cognitive Services, and hybrid architectures.
  • AI Accelerators: Develop and maintain reusable AI assets to simplify and speed up customer adoption.
  • CxO Advisory: Advise executives on AI strategy, responsible AI, governance, and organizational readiness.
  • Technical Enablement: Coach and empower customer engineering teams to scale AI capabilities sustainably.
  • Open-Source Contributions: Publish and maintain open-source AI accelerators available to the wider Azure community.

Customer Engagement Highlights

  • Manufacturing Sector: Led a major AI transformation for a global manufacturing client, building GenAI infrastructure and deploying solutions at scale, driving over $100M ARR in GPU and cloud consumption.
  • Government Sector: Delivered a national-scale AI transformation project focused on transcription, summarization, knowledge tracking, and RAG applications, successfully deployed across 200+ UK councils (LinkedIn Post, GitHub - Sonic Brief).
  • Insurance Sector: Led the transformation of underwriting processes for a major life insurance company, replacing manual assessments with AI-powered agentic workflows for faster and more consistent risk evaluation.
  • Healthcare / Pharma: Partnered with a leading multinational consumer healthcare company to build and modernize their AI platform, automating document review, data extraction, and formulation simulation across R&D processes.
  • Architecture & Engineering: Co-led the Generative Architecture Identity initiative to develop an AI-powered design tool reflecting a distinctive architectural brand identity, enabling rapid generation of concept designs and supporting a global 2025 digital transformation strategy.

Key Achievements

  • Reduced customer AI project timelines by 50% using AI accelerators.

  • Increased model prediction accuracy by up to 25% through iterative optimization.

  • Improved data processing pipelines, cutting ETL processing times by 50%.

  • Achieved up to 20% cost savings on Azure cloud spend via architectural optimization.

  • Published open-source projects including:

Complex Challenges Solved

  • Simplified AI adoption for heavily regulated industries through governance and compliance-first architectures.
  • Aligned technical solutions with business KPIs to ensure maximum executive buy-in.
  • Addressed fragmented enterprise data landscapes, building integrated data pipelines that feed AI workloads.
  • Enabled customers to deploy multi-modal AI solutions across structured and unstructured data domains.

Leadership Lessons

  • Enterprise AI transformation is complex, requiring alignment across business objectives, technical architecture, and operational processes.
  • Securing AI implementations at enterprise scale is highly challenging due to evolving regulations, privacy concerns, and governance requirements.
  • Building proof-of-concepts is relatively easy; scaling AI solutions into resilient, production-grade systems remains one of the biggest enterprise hurdles.
  • While AI capabilities are powerful, ensuring solutions are cost-effective and generate sustainable ROI is the ultimate success factor for long-term adoption.

Looking Forward

My next mission is to continue leading enterprise AI transformations, partner with business leaders to solve their highest-stake challenges, and architect scalable, responsible AI-first organizations capable of thriving in the AI economy.