Scott Horsfield
Senior Director Of Engineering Operations
Portland, Oregon Metropolitan Area United States
Summary
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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 Agile Management - IT, Master Certificate Agile Management - IT at Villanova University
Github Skills (12)
xgboost10
machine-learning10
amazon-sagemaker10
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.
amazon-ec2spot-instances
horsfieldsa/example-corp-app
Apr 2019 - Apr 2021
Contributions:2 releases, 6 pushes, 1 branch in 2 years
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