Scott Horsfield
Solutions Architecture Leader at Glean
Portland, Oregon Metropolitan Area United States
Summary
🤩
Rockstar🎓
Top SchoolScott Horsfield is a Solutions Architecture Leader based in Portland with 11 years of experience designing and scaling cloud, compute, and AI infrastructure for enterprises and government customers. He has led specialist and principal architect teams at AWS, Stripe, and Glean, driving adoption of EKS, EC2 Auto Scaling, and LLM/agent integrations while mentoring cross-functional teams. Scott blends hands-on ML engineering — including SageMaker notebooks and Spot Instance cost optimization contributions to awslabs/ec2-spot-labs — with product-focused architecture for air-gapped, cleared environments and large-scale financial and streaming platforms. Comfortable translating executive strategy into deployable reference architectures, he repeatedly builds demos, POCs, and internal tooling to accelerate customer value. His background in the USMC and DoD programs informs a disciplined security-first approach to complex, regulated environments.
11 years of coding experience
26 years of employment as a software developer
Master's degree, Management Information Systems, General, Master's degree, Management Information Systems, General at University of PhoenixMaster Certificate, Information Technology Project Management, Master Certificate, Information Technology Project Management at Villanova University
Github Skills (12)
amazon-sagemaker10
machine-learning10
xgboost10
aws10
python10
multiclass-classification9
s3-bucket9
amazon-s39
aws-s39
boto9
time-series-forecasting9
jupyter-notebook8
Programming languages (4)
Github contributions (5)
awslabs/ec2-spot-labs
Jul 2019 - Jan 2021
Collection of tools and code examples to demonstrate best practices in using Amazon EC2 Spot Instances.
Role in this project:
ML Engineer
ML EngineerContributions:1 review, 16 commits, 18 PRs in 1 year 6 months
Contributions summary:Scott contributed several notebooks that demonstrate the use of Amazon SageMaker for machine learning tasks, specifically focusing on time series forecasting with DeepAR and multiclass classification with XGBoost. The contributions involve preparing data, training models using Amazon SageMaker Managed Spot Training, and setting up the necessary infrastructure for these tasks, with a focus on leveraging Spot Instances for cost optimization. The user also created examples for utilizing and interacting with S3, boto3, and other AWS resources.
instancesecsec2-spot-instancesec2amazon-ec2
aws-samples/ec2-spot-interruption-dashboard
May 2020 - Oct 2021
This is a sample solution for logging EC2 Spot Instance Interruptions, storing them in CloudWatch and S3, and visualizing them with a CloudWatch Dashboard and Amazon Athena.
Contributions:14 commits, 4 PRs, 8 pushes in 1 year 5 months
ec2athenacloudwatch-dashboardamazonstoring
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