AI Engineer · Fractional CTO · Entrepreneur
Brandy Guillory
AI Engineer, Fractional CTO — Workforce Intelligence Group, Entrepreneur, and Thought Leader building at the intersection of AI governance, MLOps, and enterprise innovation.
AI Engineer, Fractional CTO — Workforce Intelligence Group, Entrepreneur, and Thought Leader building at the intersection of AI governance, MLOps, and enterprise innovation.
I enter complexity, create structure, align people and execution — and outcomes improve. That pattern has held across 30 years in tech and 16.5 years at IBM, from mainframe engineering to enterprise AI architecture.
I've designed end-to-end AI solutions for enterprise clients, rescued failing AI initiatives under pressure, and influenced multi-million dollar deals through technical credibility. I've supported 70+ SMB clients across industries, specializing in Financial Services where I worked with banks generating revenue exceeding $100M yearly. I built enablement programs for 300+ global attendees and mentored the next generation of engineers along the way.
My work has been recognized with IBM's Eminence & Excellence Award, 7 Manager's Choice Awards, IBM STAR Leadership selection, and the McKinsey Senior Management Accelerator. I don't just build systems — I build the people and operating models that make them last.
Leading technology strategy and innovation for AI-powered workforce solutions. Driving enterprise modernization and responsible AI adoption.
Designing end-to-end AI solution architectures for enterprise clients. Driving customer efficiency improvements, cost reductions, and measurable ROI. Rescued failing AI initiatives — improving model accuracy from 20% to 80%+.
Led strategic IT/AI roadmaps, infrastructure modernization, and cloud migrations. Managed 70+ SMB clients across industries, specializing in Financial Services with banks generating revenue exceeding $100M yearly, contributing to $2M ACV. Built 12 executive dashboards and drove 25-35% reduction in unworked CTAs.
Led migrations, resolved large-scale defect backlogs, and delivered global technical enablement. Co-led bootcamps for 300+ global attendees with hands-on labs.
Built automation and testing frameworks for mainframe systems. Resolved 1,000+ defects in DB2 tools for z/OS, developed 29 automated scripts, and converted legacy assembler logic to Java.
Provided deep technical troubleshooting for complex enterprise systems. Remediated 50+ field-reported issues and built the engineering discipline that everything else stands on.
AI models were at 20% accuracy weeks before go-live. Led rapid technical intervention — retrained models, improved training data quality, optimized configuration. Launch proceeded on schedule.
Client needed to cut costs while improving customer experience through a virtual assistant. Designed end-to-end architecture with measurable KPIs — delivered performance gains and cost reduction simultaneously.
Supported multi-million dollar enterprise deals including an $8.4M multi-year agreement, $2M ACV across 70 accounts, and multiple six-figure expansions — all driven by technical alignment and trust.
Co-led a global bootcamp, treating enablement as a product delivery problem. Built standardized labs, RACI models, and live support infrastructure that became the repeatable framework for future programs.
When platform roadmap delays created churn risk, proposed alternate deployment paths using available capabilities. Customer retained, solution implemented, defection risk eliminated.
Resolved 1,000+ defects in enterprise database tools, developed 29 automated scripts, and remediated 50+ field-reported issues. Built the engineering discipline that everything else stands on.
Designing compliance workflows, risk frameworks, and model monitoring systems. Making responsible AI the default, not the afterthought.
End-to-end model lifecycle management — from training to deployment to decommissioning — with full auditability.
Building phased training programs that take teams from dashboard basics to advanced exception handling and autonomous workflows.
Advising enterprise organizations on AI modernization, cloud migration, and operationalizing data science at scale.
Crafting technical demos that translate complex AI capabilities into clear business value for clients and stakeholders.
Publishing insights on AI augmentation, prompt engineering, and the evolving role of human expertise in automated systems.
Applied AI, developer enablement, practical tooling, and data science methodology. Arctic Code Vault Contributor.
A web app to test Hugging Face LLMs — experiment with open-source models directly from the browser.
View Repo →A web app to test Hugging Face Inference APIs — rapid prototyping with hosted model endpoints.
View Repo →AI-powered workflow automation using n8n — enterprise integration and orchestration patterns.
In ProgressAI-driven insurance agent — intelligent automation for policy analysis, claims processing, and customer engagement.
In ProgressNew project in the works — stay tuned for another applied AI solution dropping soon.
In ProgressDesigned and delivered a comprehensive AI solution for a large enterprise — full architecture from data ingestion through model deployment and monitoring.
Built an agent operations framework showcasing autonomous AI workflows, orchestration patterns, and governance guardrails for enterprise deployment.
Deployed a conversational AI assistant for a healthcare organization during the pandemic — real-world AI solving real-world problems under pressure.
Designed and led a virtual hands-on lab teaching automation capabilities and AI orchestration workflows to enterprise teams.
Led migration of 300 applications to a production containerized environment — the organization's first large-scale cloud-native deployment.
Achieved 75% containment, 88% intent recognition, and 90% first-contact resolution. Reduced costs 30% while acquiring more customers.
How smart teams leverage AI to amplify human expertise, not replace it.
Read on Medium →From human-guided development to self-healing systems.
Read on Medium →The surprising parallel between poor AI prompting and poor human communication.
Read on Medium →Information security strategies for organizations running OpenShift in regulated environments.
Read on Medium →Analyzing user feedback and escalation patterns to improve conversational AI performance.
Read on Medium →K-Means clustering and Folium data visualization for IBM Data Science capstone project.
Read on Medium →Shareable resources, frameworks, and reference materials. (Upload your docs & link them here)
Full professional background and credentials
PDF — DownloadReference architecture for responsible AI
PDFEnd-to-end AI solution design examples
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