Senior Principal Software Engineer - Red Hat AI ecosystem engineeing team.
Michael Dawson
Michael Dawson is a Senior Principal Software Engineer at Red Hat, with a focus on building AI and LLM applications as part of the ecosystem engineeing team. Before moving into the AI space, he was a key contributor to Node.js as the technical lead for IBM and Red Hat's Node.js team and a member of the Node.js Technical Steering Committee. Going back further his experience includes building IBM's Java runtime, building and operating client facing e-commerce applications, building PKI and symmetric based crypto solutions as well as a number of varied consulting engagements. In his spare time, he uses Node.js to automate his home and life for fun.
Michael Dawson's contributions
Article
Automate AI agents with the Responses API in Llama Stack
Learn how the Responses API in Llama Stack automates complex tool calling while maintaining granular control over conversation flow for AI agents. Discover the benefits and implementation details.
Article
Prompt engineering: Big vs. small prompts for AI agents
Explore big versus small prompting in AI agents. Learn how Red Hat's AI quickstart balances model capability, token costs, and task focus using LangGraph.
Article
AI quickstart: Self-service agent for IT process automation
Discover the self-service agent AI quickstart for automating IT processes on Red Hat OpenShift AI. Deploy, integrate with Slack and ServiceNow, and more.
Article
How to implement observability with Python and Llama Stack
Enhance your Python AI applications with distributed tracing. Discover how to use Jaeger and OpenTelemetry for insights into Llama Stack interactions.
Article
Implement AI safeguards with Python and Llama Stack
Learn how to implement Llama Stack's built-in guardrails with Python, helping to improve the safety and performance of your LLM applications.
Article
Retrieval-augmented generation with Llama Stack and Python
This tutorial shows you how to use the Llama Stack API to implement retrieval-augmented generation for an AI application built with Python.
Article
Exploring Llama Stack with Python: Tool calling and agents
Harness Llama Stack with Python for LLM development. Explore tool calling, agents, and Model Context Protocol (MCP) for versatile integrations.
Article
How to implement observability with Node.js and Llama Stack
Enhance your Node.js AI applications with distributed tracing. Discover how to use Jaeger and OpenTelemetry for insights into Llama Stack interactions.
Automate AI agents with the Responses API in Llama Stack
Learn how the Responses API in Llama Stack automates complex tool calling while maintaining granular control over conversation flow for AI agents. Discover the benefits and implementation details.
Prompt engineering: Big vs. small prompts for AI agents
Explore big versus small prompting in AI agents. Learn how Red Hat's AI quickstart balances model capability, token costs, and task focus using LangGraph.
AI quickstart: Self-service agent for IT process automation
Discover the self-service agent AI quickstart for automating IT processes on Red Hat OpenShift AI. Deploy, integrate with Slack and ServiceNow, and more.
How to implement observability with Python and Llama Stack
Enhance your Python AI applications with distributed tracing. Discover how to use Jaeger and OpenTelemetry for insights into Llama Stack interactions.
Implement AI safeguards with Python and Llama Stack
Learn how to implement Llama Stack's built-in guardrails with Python, helping to improve the safety and performance of your LLM applications.
Retrieval-augmented generation with Llama Stack and Python
This tutorial shows you how to use the Llama Stack API to implement retrieval-augmented generation for an AI application built with Python.
Exploring Llama Stack with Python: Tool calling and agents
Harness Llama Stack with Python for LLM development. Explore tool calling, agents, and Model Context Protocol (MCP) for versatile integrations.
How to implement observability with Node.js and Llama Stack
Enhance your Node.js AI applications with distributed tracing. Discover how to use Jaeger and OpenTelemetry for insights into Llama Stack interactions.
