Architect, AI - Active Top Secret Clearance

Red Hat•Remote US VA, VA

2d•$130,420 - $215,300

About The Position

Red Hat Services is seeking an AI Architect to design and deliver strategic customer solutions through technical leadership, hands-on implementation, and enablement of recommended practices. The role promotes adoption of Red Hat AI platforms and solutions and combines technical expertise, team leadership, open-source awareness, understanding of business processes, and enterprise-level problem solving. Red Hat Consulting works as a strategic adviser to customers, helping them address organizational challenges and build enterprise-wide capabilities with comprehensive, cost-effective solutions. The position involves working on-site with clients and traveling regularly throughout North America, including weekly Monday–Thursday travel according to project needs. Candidates must reside in a state where Red Hat is registered to do business and hold an active Top Secret clearance (TS/SCI CI Poly). The architect will guide model selection, optimization, and serving; design inference, retrieval, evaluation, and sovereign deployment patterns; and build AI services and agentic systems on Red Hat platforms. They will lead customer engagements, set measurable success criteria, enable customer engineers to operate delivered solutions, create reusable practice assets, provide product feedback, and represent Red Hat’s enterprise AI perspective to customer executives and technical leaders.

Requirements

10 or more years of experience in enterprise technology, including at least 5 years as a hands-on architect or senior consultant delivering in customer environments.
Active Top Secret clearance (TS/SCI CI Poly).
Residence in a state where Red Hat is registered to do business.
Deep, current OpenShift expertise, including 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 upstreams, 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, RAG and evaluation, agent frameworks and harnesses, MCP, and production governance and cost pressures.
Application development fluency on Kubernetes, including containers, APIs, CI/CD, and trusted software supply chains such as SBOMs, signing, and policy gates, in at least one of Python, Go, or Java.
Experience automating with Ansible Automation Platform, including event-driven automation, or equivalent experience that can be mapped to it quickly.
A record of leading engagements to measurable results by a defined date and enabling customer teams to operate the delivered solution.
Clear written and verbal communication for technical and executive audiences, including setting success metrics with sponsors and reporting results.
Willingness to travel regularly to customer locations throughout North America.

Nice To Haves

Production agent development experience, including tool calling, agent identity and delegation, sandboxed execution, agent observability and evaluation, or 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.
AI FinOps experience, including cost baselines, unit economics per use case, and verification of 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 experience at a platform or model company.

Responsibilities

Help customers adopt frontier and open models using their own models, data, and AI approaches.
Lead model selection, optimization, and serving decisions on Red Hat AI, including vLLM, llm-d, LLM Compressor, the validated model catalog, and hardware profiles across NVIDIA, AMD, and other accelerators.
Establish inference cost baselines and reduce costs per use case using GuideLLM benchmarks and recipe-based optimization; determine whether prompting, grounding, or tuning best fits a task.
Design portable inference workloads that can move between cloud APIs, on-premises clusters, and sovereign environments without re-architecture.
Travel regularly, potentially weekly Monday through Thursday, to client locations and work in customers’ environments.
Design evaluation-driven release gates in Eval Hub using customer-defined tests.
Architect retrieval and context patterns, including RAG, knowledge ingestion, and permission-aware context.
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, AI gateways, metering, showback and chargeback, and self-service accelerator access.
Build agentic systems that operate infrastructure through Ansible Automation Platform, with actions governed by role-based access control, approvals, and audit.
Apply agent frameworks and protocols such as MCP, A2A, OpenAI-compatible APIs, Llama Stack, and LangGraph, designing identity, policy, and observability into solutions.
Own engagement architecture and delivery plans, define success metrics with customer sponsors, and hold teams accountable for results.
Pair with and certify customer engineers so delivered solutions can be operated after the engagement.
Create reusable practice assets, including runbooks, reference architectures, harnesses, and recipes, and provide evidence-based product feedback to Red Hat engineering.
Represent Red Hat’s perspective on enterprise AI to CTOs, platform leaders, and FinOps teams, and support sellers in scoping outcomes with reliable timelines.
Provide customer feedback to internal teams to improve products and services.
Lead internal initiatives that foster innovation, efficiency, and growth within the Services business.
Continuously learn and adapt to new skills and technologies.

Benefits

Comprehensive medical, dental, and vision coverage.
Flexible Spending Account for healthcare and dependent care.
Health Savings Account for a high-deductible medical plan.
401(k) retirement plan 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.
Employee stock purchase plan.
Family planning reimbursement.
Tuition reimbursement.
Transportation expense account.
Employee assistance program.