The Red Hat Services team is looking for an AI Architect to design and deliver strategic solutions that solve our clients’ business challenges, through technical leadership, hands‑on implementation, and enablement of recommended practices. In this role, you will play a key role as a promoter of the adoption of Red Hat's AI platforms and solutions by using your technical expertise and interpersonal skills.
You’ll need to have a balance of solid technical experience, team leadership, awareness of open source, thorough understanding of business processes, and the ability to identify and solve issues at an enterprise level.
Red Hat Consulting offers more than just technical expertise. We're strategic advisers who take a big picture view of our customers’ organizations, understand their challenges, and help to overcome them with comprehensive, cost-effective solutions. As part of our team, you'll help customers quickly and strategically achieve value while building enterprise-wide capabilities. Being open is core to our technologies and our consulting approach.
We want to be transparent about what would make you most successful in the role.
This position requires candidates to work on‑site with clients, so a willingness to travel regularly to customer locations throughout North America is required. Successful applicants must reside in a state where Red Hat is registered to do business and hold an Active Top Secret Clearance (TS/SCI CI Poly).
What You Will DoOur customers want to make AI their own: their model, their data, their AI.
Help customers make frontier and open models their own
- Lead model selection, optimization and serving decisions on Red Hat AI: vLLM, llm-d, LLM Compressor, the validated model catalog, and hardware profiles across NVIDIA, AMD and other accelerators.
- Baseline and reduce inference cost per use case (cost per million tokens, GPU utilization) using GuideLLM benchmarks and recipe-based optimization, and decide 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.
- Travel weekly, Monday-Thursday, to client locations
Ground and evaluate on the customer's terms, in the customer's environment
- Design evaluation‑driven release gates in Eval Hub so a model or agent ships only when the customer's own tests say so.
- Architect retrieval and context patterns (RAG, knowledge ingestion, permission‑aware context) that keep data where it belongs, and know when that work is repeatable and when it is bespoke.
- Lead sovereign and disconnected deployments of Red Hat AI on Open Shift, including model provenance, signing, AI bills of materials and compliance evidence.
Run it as a service, and run operations with it
- Design and stand up Models‑as‑a‑Service and GPU‑as‑a‑Service on Open Shift AI and Red Hat AI Enterprise: multi‑tenancy, the AI gateway, metering, showback and chargeback, self‑service accelerator access.
- Build agentic systems that operate infrastructure through Ansible Automation Platform: patching, CVE remediation, upgrades, supply‑chain attestation and incident remediation, with every action passing through RBAC, approval and audit.
- Apply agent frameworks and protocols as customers use them today (MCP, A2A, OpenAI‑compatible APIs, Llama Stack, Lang Graph and similar), with identity, policy and observability designed in from the start.
Lead the engagement and leave it better
- Own the architecture and the delivery plan on engagements from two‑week baselines to multi‑month service builds and lighthouse programs; set the success metric with the customer's sponsor and hold the team to it.
- Pair with and certify customer engineers so the result…
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