Jerry Walton

Designed and implemented a multi-language (Python, Go, Rust) data processing and AI-assisted

• • pipeline for the M38 architecture, progressing from proof-of-concept to validated large-scale execution. Built end-to-end pipeline components including ingestion, normalization, recursive delta-windowing, compression kernels, deterministic reconstruction, and full instrumentation. Executed and validated a 1 GB deterministic pipeline run, producing confirmable compression/ decompression results, performance metrics, trace logs, and scalability analysis. Developed detailed documentation covering algorithmic structure, recursion models, data handling strategies, validation methodology, and scaling pathways toward multi-GB datasets. Extended the architecture with AI-assisted analysis and optimization concepts, incorporating predictive behavior, instrumentation expansion, and performance-tuning strategies. Demonstrated system correctness, reproducibility, and readiness for scale under strict technical review.

Designed and optimized large-scale data pipelines in Python and Go, processing 140M+ entities

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• • under strict performance constraints. Built and deployed cloud-native backend services on AWS supporting high-throughput ingestion and transformation workflows. Integrated analytics and search pipelines to support downstream intelligence and AI-adjacent use cases. Emphasized observability, failure analysis, and production reliability.

Built Python/FastAPI services supporting AI-driven workflows on AWS.

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• • Implemented ingestion, preprocessing, and storage pipelines for model-facing datasets. Automated infrastructure and deployments using Terraform.