AI Engineer
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Enterprise open-source software, hybrid cloud, and AI infrastructure

Red Hat

Red Hat develops enterprise open-source software for running applications and managing IT infrastructure. Its portfolio spans Red Hat Enterprise Linux, the OpenShift Kubernetes platform, Ansible Automation Platform, and Red Hat AI. These technologies help organizations develop cloud-native applications, automate operations, and manage hybrid cloud environments. OpenShift AI and Red Hat Inference Server extend that infrastructure into AI model development and deployment, while Red Hat AI lets customers retain control over their data, models, and deployment decisions.

Founded in 1993 by Bob Young and Marc Ewing, Red Hat is led by president and CEO Matt Hicks, who took the role in 2022. Its engineering approach connects upstream open-source communities with commercially supported products. One concrete contribution is Podman, a daemonless tool for managing and running Linux containers. Red Hat Developer Hub builds on Backstage to bring developer tools, repositories, documentation, and reusable software templates into an internal portal.

Red Hat is an IBM subsidiary following IBM’s completed 2019 acquisition, valued at approximately $34 billion in equity. Its subscription offering includes technical support, lifecycle guidance, and access to multiple supported versions, allowing customers to choose when to upgrade. The company reported that more than 90% of Fortune 500 companies were customers, based on client data and the September 2025 Fortune 500 list.

www.redhat.com ↗

3 talks

Newest first

3 speakers at AIE

Affiliations reflect their AIE appearances, not necessarily current employment.

Sally Ann O'MalleyTaylor Jordan SmithPrincipal developer advocateCedric Clyburn

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  1. Lobster Trap: OpenClaw in Containers from Local to K8s and Back

    Learn how agent tools, skills, and MCP servers are packaged for movement between local Podman, Kubernetes, and OpenShift.

    Sally Ann O'MalleyAI Engineer Europe 2026

  2. Strategies for LLM Evals (GuideLLM, lm-eval-harness, OpenAI Evals Workshop) — Taylor Jordan Smith

    Start here to connect evaluation planning with RAG and agent deployments, inference runtimes, reliability, and risk mitigation.

    Taylor Jordan SmithAI Engineer World's Fair 2025

  3. Structuring the Unstructured: Advanced Document Parsing for AI Workflows

    Learn how locally runnable Docling produces Markdown, JSON, HTML, and Pydantic representations for retrieval and agent applications.

    Cedric ClyburnAI Engineer World's Fair 2026

Messages from the stage

Agent state and secrets across environments

Sally Ann O'Malley explains how Podman secrets and OpenClaw secret references handle credentials, while volumes preserve runtime state for backup and recovery.

Evaluate load alongside model behavior

Taylor Jordan Smith pairs GuideLLM request-rate benchmarking with exercises covering MMLU-Pro, safety, bias, and custom evaluations.

Preserve context during document extraction

Cedric Clyburn explains how OCR, vision models, layout-aware parsing, and table and image extraction preserve document context while addressing privacy and cost concerns.

Affiliations reflect each recorded session, not necessarily current employment.

Company sources · checked 2026-08-27