Michael Norman

Designed and deployed an agentic AI automation platform to automate business workflows across internal applications and enterprise services. Built production AI agents using Python, FastAPI, LangGraph, LLM tool calling, and structured outputs to retrieve business data, execute approved actions, and coordinate multi-step workflows. Integrated agents with REST APIs, webhooks, databases, email, Slack/Jira/CRM systems and n8n-style workflows, reducing manual processing time by ~30% and shortening workflow completion time. Implemented human-in-the-loop approvals, retry/fallback strategies, validation, access controls, and audit logging for sensitive and high-impact automated actions. Deployed AI services on AWS (Bedrock, Lambda, ECS/EKS, S3, RDS, API Gateway, EventBridge, SQS, IAM, CloudWatch) to support scalable and fault-tolerant production workloads. Built enterprise search and RAG pipelines using Elasticsearch, embeddings, vector retrieval, and metadata filtering. Developed LLM evaluati

Built an agentic AI automation platform to automate workflows across internal applications and enterprise services, reducing manual processing time by 30%. Developed production AI agents using Python, FastAPI, LangGraph, LLMs, tool calling, MCP, and structured outputs to retrieve business data, execute approved actions, and coordinate multi-step workflows. Integrated agents with REST APIs, webhooks, databases, email, Slack, Jira, CRM systems, and n8n to cut workflow completion time. Implemented human-in-the-loop approvals, retry/fallback strategies, validation, access controls, and audit logging. Deployed AI services on AWS (Bedrock, Lambda, ECS/EKS, S3, RDS, API Gateway, EventBridge, SQS, IAM, CloudWatch) for scalable, fault-tolerant production workloads. Designed RAG and enterprise search pipelines with Elasticsearch, embeddings/vector retrieval, and metadata filtering. Implemented LLM evaluation and observability frameworks for response quality, latency, failure rates, token usage,

Designed and delivered enterprise networking, security, observability, and cloud management solutions using C#, .NET, ASP.NET Core, TypeScript, and JavaScript. Built network management and observability platforms with centralized monitoring of networking, security, and cloud infrastructure. Developed scalable backend services with C#, .NET Core, Entity Framework Core, and SQL Server to collect, process, and expose real-time telemetry from distributed network devices and cloud systems. Created responsive monitoring dashboards with TypeScript, JavaScript, Angular/React, HTML5, and CSS3 to provide real-time visibility into device health, security alerts, performance metrics, and operational status. Implemented high-performance RESTful APIs using ASP.NET Core and .NET middleware to improve communication across frontends, network services, and cloud management platforms. Built real-time monitoring and notification features using asynchronous C# services, .NET background services, SignalR, a