Dan Ferguson

Working with AWS's largest enterprise customers to design and harden cutting-edge inference architectures through open-source integrations with Amazon SageMaker AI. This role sits at the frontier of ML inference — where model performance meets production reliability at scale.

I develop expert-level technical content, including blog posts and open-source tooling, that AWS field teams and customers use to build and mature their inference systems. My work focuses on bridging the gap between research-grade ML and enterprise-grade deployment.

Served as the trusted AI/ML advisor for Private Equity firms and their portfolio companies, helping them leverage ML and generative AI technologies within their 5–7 year value creation roadmaps. This role spanned the full spectrum from strategic alignment with Operating Partners to hands-on architecture and prototyping with engineering teams.

As the lead ML Specialist Solutions Architect for PE in the NAMER region, I built the playbook for AI/ML engagement across the PE landscape — then trained a cohort of generalist Solutions Architects to scale it. I cultivated a partner network of ML-focused delivery firms that extended our reach across dozens of portfolio companies. These efforts directly enabled our team to exceed our generative AI proof-of-concept program goal by more than 3x.

Alongside customer work, I contributed to the AWS knowledge base as a Subject Matter Expert: co-authoring Well-Architected papers for ML and Generative AI, writing technical blog posts, contributing to open-source repositories, and coaching colleagues as a trained speaker bar-raiser.

Played a central role in establishing and maturing Ippon's data practice, introducing strategic frameworks for data-driven solutions and leading a team of consultants through hands-on engagements. I scoped architectures, led delivery teams, drafted business proposals, and conducted AWS Well-Architected Reviews across Ippon's client portfolio.

My client engagements during this period spanned data platform development, event-driven architectures, cloud data warehousing, and ETL modernization for companies ranging from early-stage startups to enterprise healthcare and financial services firms.

Led critical infrastructure and tooling initiatives within Capital Markets, managing a global low-latency message bus spanning six data centers. This role combined deep systems engineering — troubleshooting network issues, RHEL server configurations, and Solace messaging appliances — with multi-language software development in Java, Perl, PHP, C#, and Python.

I developed production monitoring tools, automated global failover orchestration, pioneered chatbot integrations on the Symphony platform, and deployed distributed analytics stacks across regions — establishing operational practices that improved reliability and transparency across the division.

Started my career developing automation scripts for barcode scanner testing in C# and building data visualization software for performance benchmarking. My log analysis and visualization application — my first project as a lead developer — remained in production for several years after my departure, and served both engineering and marketing teams.