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Jason Layton

@jasonlayton

Senior AI Engineer at Jefferies who built financial document AI processing that reduced manual review workloads by 60%.

At Jefferies, I architected an AI document processing platform using Azure OpenAI, Azure AI Search, and LangGraph that reduced manual financial review workloads by 60%. I also built RAG pipelines that achieved 92% retrieval accuracy across SEC filings and financial reports.

At Johnson & Johnson, I developed HIPAA-compliant clinical decision support systems and conversational AI assistants for healthcare support teams. Earlier, at Badger Technologies, I enhanced retail recommendations with LLM-driven personalization, and at HCLTech, I built distributed backend systems and REST APIs.

Experience

Work history, roles, and key accomplishments

Senior AI Engineer

Jefferies

Sep 2023 - Aug 2026 (2 years 11 months)

Architected enterprise-grade AI document processing platforms using Azure OpenAI, Azure AI Foundry, Azure AI Search, and LangGraph, reducing manual financial review workloads by 60%. Designed and deployed multi-agent orchestration systems for automated market intelligence generation, supporting over 200 analysts and executives.

Azure OpenAI Azure AI Foundry Azure AI Search LangGraph RAG Multi Agent Systems MCP Langsmith azure monitor Kubernetes HIPAA HL7 FHIR

AI Engineer

Johnson & Johnson

Nov 2021 - Aug 2023 (1 year 9 months)

Developed HIPAA-compliant clinical decision support systems using Amazon Bedrock, SageMaker, and LangChain to improve patient risk assessment and treatment recommendations. Built AI-powered clinical document intelligence pipelines using RAG, LangChain, pgvector, and XGBoost.

Amazon Bedrock Sagemaker Lang Chain RAG Pgvector XGBoost AWS Lambda EventBridge Step Functions HIPAA Cloudwatch

Full-Stack Engineer

Badger Technologies

Aug 2019 - Oct 2021 (2 years 2 months)

Enhanced AI-powered retail recommendation systems using LLM-driven personalization and semantic product retrieval, increasing recommendation accuracy by 28%. Developed real-time streaming data pipelines using Python, Spark, Kafka, and AWS Kinesis to process over 20M daily customer interaction events.

Python Spark Kafka AWS Kinesis Elasticsearch Redis Airflow Docker CI CD A B Testing

Software Engineer

HCLTech

Aug 2017 - Jul 2019 (1 year 11 months)

Built enterprise-scale distributed backend systems using .NET, Node.js, and microservices architecture supporting more than 500K daily business transactions. Designed REST APIs and event-driven messaging systems with RabbitMQ and asynchronous processing patterns.