Job description

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 are looking for people excited about working with us and growing their careers at Red Hat. We want to be transparent about what would make you most successful in the role, and if you are excited by reading the job description and feel you are suitable for the part, we encourage you to apply. 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 ( T S/SCI CI Poly) . What you will do Our 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 OpenShift, 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 OpenShift 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, LangGraph 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 keeps running after we leave. Turn each delivery into a reusable asset for the practice: runbooks, reference architectures, harnesses and recipes, and feed product gaps back to Red Hat engineering with evidence. Represent Red Hat s point of view on enterprise AI with 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 Regular travel to client locations, up to weekly according to project needs What you bring 10 years in enterprise technology, with at least 5 as a hands-on architect or senior consultant delivering in customer environments. Deep, current OpenShift expertise: cluster architecture, GitOps, multi-cluster management, security and observability, GPU-enabled nodes; Red Hat Certified Architect or equivalent demonstrated depth. Production experience with Red Hat AI or its upstreams: 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 how enterprises run generative AI today: inference economics, model selection and optimization, RAG and evaluation, agent frameworks and harnesses, MCP, and the governance and cost pressures that shape production decisions. Application development fluency on Kubernetes: containers, APIs, CI/CD and the trusted software supply chain (SBOM, signing, policy gates), in at least one of Python, Go or Java. Automation with Ansible Automation Platform, including event-driven automation, or an equivalent you can map onto it quickly. A record of leading engagements to a measurable result on a date, and of leaving customer teams able to run what you built. Clear writing and speaking for technical and executive audiences; comfortable setting a success metric with a sponsor and reporting against it. Preferred Agent development in production: tool calling, agent identity and delegation, sandboxed execution, agent observability and evaluation; contributions to MCP, A2A, Llama Stack, LangGraph, Kagenti or similar. Experience designing Models-as-a-Service or GPU-as-a-Service for shared platform teams: multi-tenancy, gateways, metering, showback and chargeback, scheduling with Kueue or Run:ai. FinOps for AI: building cost baselines, unit economics per use case, and verifying savings on a customer s own hardware. Sovereign, regulated or disconnected deployments; familiarity with EU AI Act obligations and model provenance requirements. Model customization: fine-tuning and post-training, synthetic data generation, and the judgment to know when prompting or grounding is the better answer. Open source contribution and community standing in projects Red Hat leads or sponsors. Prior forward-deployed, residency or customer-engineering work at a platform or model company. The salary range for this position is $130,420.00 - $215,300.00. Actual offer will be based on your qualifications. Pay Transparency Red Hat determines compensation based on several factors including but not limited to job location, experience, applicable skills and training, external market value, and internal pay equity. Annual salary is one component of Red Hat’s compensation package. This position may also be eligible for bonus, commission, and/or equity. For positions with Remote-US locations, the actual salary range for the position may differ based on location but will be commensurate with job duties and relevant work experience. About Red Hat Red Hat is the world’s leading provider of enterprise open source software solutions, using a community-powered approach to deliver high-performing Linux, cloud, container, and Kubernetes technologies. Spread across 40 countries, our associates work flexibly across work environments, from in-office, to office-flex, to fully remote, depending on the requirements of their role. Red Hatters are encouraged to bring their best ideas, no matter their title or tenure. We re a leader in open source because of our open and inclusive environment. We hire creative, passionate people ready to contribute their ideas, help solve complex problems, and make an impact. Benefits ● Comprehensive medical, dental, and vision coverage ● Flexible Spending Account - healthcare and dependent care ● Health Savings Account - high deductible medical plan ● Retirement 401(k) with employer match ● Paid time off and holidays ● Paid parental leave plans for all new parents ● Leave benefits including disability, paid family medical leave, and paid military leave ● Additional benefits including employee stock purchase plan, family planning reimbursement, tuition reimbursement, transportation expense account, employee assistance program, and more! Note: These benefits are only applicable to full time, permanent associates at Red Hat located in the United States. Inclusion at Red Hat Red Hat’s culture is built on the open source principles of transparency, collaboration, and inclusion, where the best ideas can come from anywhere and anyone. When this is realized, it empowers people from different backgrounds, perspectives, and experiences to come together to share ideas, challenge the status quo, and drive innovation. Our aspiration is that everyone experiences this culture with equal opportunity and access, and that all voices are not only heard but also celebrated. We hope you will join our celebration, and we welcome and encourage applicants from all the beautiful dimensions that compose our global village. Equal Opportunity Policy (EEO) Red Hat is proud to be an equal opportunity workplace and an affirmative action employer. We review applications for employment without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, ancestry, citizenship, age, veteran status, genetic information, physical or mental disability, medical condition, marital status, or any other basis prohibited by law. Red Hat does not seek or accept unsolicited resumes or CVs from recruitment agencies. We are not responsible for, and will not pay, any fees, commissions, or any other payment related to unsolicited resumes or CVs except as required in a written contract between Red Hat and the recruitment agency or party requesting payment of a fee. Red Hat supports individuals with disabilities and provides reasonable accommodations to job applicants. If you need assistance completing our online job application, email application-assistance redhat.com . General inquiries, such as those regarding the status of a job application, will not receive a reply.

Requirements

Must-haves

Our 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.
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 OpenShift, 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 OpenShift AI and Red Hat AI Enterprise: multi-tenancy, the AI gateway, metering, showback and chargeback, self-service accelerator access.
Apply agent frameworks and protocols as customers use them today (MCP, A2A, OpenAI-compatible APIs, Llama Stack, LangGraph 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 keeps running after we leave.
Turn each delivery into a reusable asset for the practice: runbooks, reference architectures, harnesses and recipes, and feed product gaps back to Red Hat engineering with evidence.
Represent Red Hat s point of view on enterprise AI with CTOs, platform leaders and FinOps teams, and support sellers in scoping results that can be dated with confidence.

Nice-to-haves

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

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aillmkubernetesci/cdpythongojava

Platform: workday
Effort: long

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Paid Time Off

Parental Leave