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Red Hat

Enterprise open-source software subscriptions and services

AI Architect

Implementation & Technical Account ManagementConfirmed live in the last 24 hours
Full-timeRemote in USA

About the Job

Requirements

  • At least 10 years of experience in enterprise technology, including at least 5 years as a hands-on architect or senior consultant delivering in customer environments.
  • Deep, current OpenShift expertise covering cluster architecture, GitOps, multi-cluster management, security, observability, and GPU-enabled nodes; Red Hat Certified Architect certification or equivalent demonstrated depth.
  • Production experience with Red Hat AI or its upstream projects, including OpenShift AI, vLLM, model serving with KServe, model registries and pipelines, and at least one of llm-d, LLM Compressor, or Eval Hub.
  • Working knowledge of enterprise generative AI, including inference economics, model selection and optimization, retrieval-augmented generation, evaluation, agent frameworks and harnesses, MCP, and production governance and cost pressures.
  • Application development fluency on Kubernetes, including containers, APIs, CI/CD, and the trusted software supply chain (SBOM, signing, and policy gates), in at least one of Python, Go, or Java.
  • Experience automating with Ansible Automation Platform, including event-driven automation, or an equivalent that can be mapped onto it quickly.
  • A record of leading engagements to a measurable result on a defined date and leaving customer teams able to operate what was built.
  • Ability to write and speak clearly for technical and executive audiences, set a success metric with a sponsor, and report against it.
  • Must hold an active Top Secret clearance (TS/SCI CI Poly).
  • Must reside in a state where Red Hat is registered to do business.
  • Must be willing to travel regularly to client locations throughout North America, potentially weekly.

Responsibilities

  • Lead model selection, optimization, and serving decisions on Red Hat AI using vLLM, llm-d, LLM Compressor, the validated model catalog, and hardware profiles across NVIDIA, AMD, and other accelerators.
  • Establish inference cost baselines and reduce cost per use case using GuideLLM benchmarks and recipe-based optimization; determine with evidence whether to prompt, ground, or tune a model for a task.
  • Design portable inference workloads that move between cloud APIs, on-premises clusters, and sovereign environments without re-architecture.
  • Design evaluation-driven release gates in Eval Hub so models or agents ship only when they pass customer-defined tests.
  • Architect retrieval and context patterns, including RAG, knowledge ingestion, and permission-aware context, while determining what is repeatable versus bespoke.
  • Lead sovereign and disconnected deployments of Red Hat AI on OpenShift, including model provenance, signing, AI bills of materials, and compliance evidence.
  • Design and establish Models-as-a-Service and GPU-as-a-Service on OpenShift AI and Red Hat AI Enterprise, including multi-tenancy, the AI gateway, metering, showback and chargeback, and self-service accelerator access.
  • Build agentic systems that operate infrastructure through Ansible Automation Platform for patching, CVE remediation, upgrades, supply-chain attestation, and incident remediation, with actions controlled by RBAC, approval, and audit.
  • Apply agent frameworks and protocols used by customers, including MCP, A2A, OpenAI-compatible APIs, Llama Stack, and LangGraph, designing identity, policy, and observability into the systems.
  • Own engagement architecture and delivery plans, from two-week baselines to multi-month service builds and lighthouse programs; set success metrics with customer sponsors and hold the team accountable to them.
  • Pair with and certify customer engineers so delivered systems continue operating after the engagement.
  • Turn deliveries into reusable practice assets, including runbooks, reference architectures, harnesses, and recipes, and provide evidence-based product-gap feedback to Red Hat engineering.
  • Represent Red Hat’s enterprise AI perspective to CTOs, platform leaders, and FinOps teams, and support sellers in scoping results that can be dated with confidence.
  • Provide customer feedback to internal teams to enhance product offerings and services.
  • Lead internal initiatives that foster innovation, efficiency, and growth within the Services business.
  • Continuously learn, grow, and adapt to new skills and technologies.
  • Travel weekly, typically Monday through Thursday, to customer locations as project needs require.

Desired Qualifications

  • Production agent development experience, including tool calling, agent identity and delegation, sandboxed execution, agent observability, and evaluation; contributions to MCP, A2A, Llama Stack, LangGraph, Kagenti, or similar projects.
  • Experience designing Models-as-a-Service or GPU-as-a-Service for shared platform teams, including multi-tenancy, gateways, metering, showback and chargeback, and scheduling with Kueue or Run:ai.
  • FinOps experience for AI, including building cost baselines, calculating unit economics per use case, and verifying savings on customer hardware.
  • Experience with sovereign, regulated, or disconnected deployments; familiarity with EU AI Act obligations and model provenance requirements.
  • Model customization experience, including fine-tuning, post-training, and synthetic data generation, and judgment about when prompting or grounding is preferable.
  • Open source contributions and community standing in projects Red Hat leads or sponsors.
  • Prior forward-deployed, residency, or customer-engineering work at a platform or model company.

About the Company

Red Hat

Enterprise open-source software subscriptions and services

Red Hat sells enterprise open-source software with a subscription model that includes updates, security patches, and professional support. Its products—RHEL for hybrid cloud servers, OpenShift for containerized apps, and the Ansible Automation Platform for IT automation—are open-source at core but provided with guaranteed enterprise support. Red Hat differentiates itself by pairing open-source code with a deep services layer and an integrated stack designed for hybrid/multicloud environments, backed by IBM. Its goal is to help large organizations deploy, manage, and scale open-source software across complex IT landscapes with reliable software and predictable, ongoing support.

Company size
10,001+
Company stage
IPO
Headquarters
Raleigh, NC
Founded
1993

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