Randall Vaughn Shane

Designed and led the implementation of a secure, cost-optimized enterprise analytics and AI platform on AWS for JPMorgan Chase’s Commercial Banking division. The architecture unified multiple data pipelines across EMR, S3, Redshift, and Lambda, supporting NLP-driven enrichment workflows that merged internal and 3rd-party data from Dun & Bradstreet, Bloomberg, and Morningstar.

I implemented automated CI/CD with Terraform, Jenkins, and Airflow, enabling consistent data-pipeline deployments and reducing delivery cycles by 60%. I optimized EMR cluster utilization with right-sizing and Spot-instance adoption, lowering monthly infrastructure costs by over $100,000 while maintaining SLAs.

Finally, I delivered an AI-ready data foundation integrating SageMaker and PySpark for predictive risk and loan-approval analytics, accelerating credit-decision turnaround during the COVID-19 surge. The system improved operational resilience, analytics velocity, and financial governance across multiple lines of business (LOBs).

Led the architecture and integration of the US Army’s enterprise data environment, unifying operational data from G-1 (personnel), G-2 (intelligence/security), G-3 (operations/command and control), and G-4 (logistics) directorates. I implemented a master data repository and message-bus architecture enabling real-time data exchange across heterogeneous systems.

Additionally, I developed geospatial and temporal analytics pipelines that correlated troop movements, mission reports, and logistics activity with external intelligence feeds, forming the analytic foundation for predicting time and location probabilities of improvised explosive device incidents.

Finally, I delivered a secure, DoD-compliant data platform employing encryption, tokenization, and role-based access, supporting 28 engineers and analysts. The solution improved intelligence lead-times and reduced false-positive alerts by 40% in simulation trials, directly enhancing battlefield readiness.

Designed and developed a modular RAG and agent orchestration framework for enterprise-scale document intelligence and knowledge applications. I implemented a multi-agent architecture using LangChain, LangGraph, and FastAPI, supporting local LLMs, Mixtral and Llama 3, and OpenAI GPT-4o for hybrid reasoning and verification.

The system integrates vector search (FAISS), semantic tagging, and governance controls aligned with AWS Lake Formation principles. I delivered API endpoints for document summarization, semantic query, and token-level explainability. The solution powers early versions of multiple Fox River AI products, including AI-assisted diagramming, finance-ontology reasoning, and the FoxDigest subscription-based document summarization.

The system is built on a secure, containerized stack—Python, FastAPI, PostgreSQL, Neo4j, Docker, and AWS S3—and deployed via Cloudflare Tunnel for private client access. Benchmarked latency and retrieval accuracy against GPT-4 baselines, achieving over 92% relevance precision while maintaining complete local data control.

Architected a click-stream analytics platform capturing millions of digital-banking interactions on Cloudera Hadoop, enabling customer-journey segmentation and personalized marketing. I built data models and ingestion pipelines from relational sources into a unified repository supporting regulatory and marketing analytics.

These design patterns later informed retail-style personalization and behavioral analytics frameworks, which are now common in eCommerce and fintech environments.