AI Architect

RTX

. Define end-to-end architectures for enterprise AI and ML solutions spanning machine learning, Generative AI, agentic AI, data, applications, APIs, platforms, infrastructure, and enterprise systems .

Posted 9/17/2026full-timeFarmington • Arizona • United StatesSeniorLead💰 $132,400 - $251,600 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in defining and implementing end-to-end architectures for AI and ML solutions, with a strong focus on Generative AI, data integration, and enterprise systems. Proven ability to translate complex business requirements into actionable architecture blueprints and lead technical discussions across diverse teams.

Highest-signal resume keywords

AI/ML Solution ArchitectureGenerative AI ExperienceCloud-Native Application DesignEnterprise Security ConceptsTechnical Leadership and Mentorship

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningGenerative AIData Pipeline DesignMLOpsModel Lifecycle ManagementAPI DevelopmentMicroservices ArchitectureDistributed SystemsTechnical ArchitectureEnterprise Integration

Soft Skills

Technical CommunicationInfluencing Architecture DecisionsLeading Technical Discussions

Tools & Technologies

KubernetesCI/CDInfrastructure-as-CodeCloud PlatformsModel Monitoring Tools

Industry Keywords

Responsible AINIST AI RMFTOGAFZachman FrameworkIdentity and Access Management

Tech Stack

Tools & technologies

CloudCyber SecurityDistributed SystemsKubernetesMicroservices

About the role

Key responsibilities & impact

Define end-to-end architectures for enterprise AI and ML solutions spanning machine learning, Generative AI, agentic AI, data, applications, APIs, platforms, infrastructure, and enterprise systems
Translate business opportunities and requirements into architecture blueprints, technical strategies, success criteria, and implementation roadmaps
Determine technical approaches for complex business problems, including software, machine learning, Generative AI, retrieval-augmented generation, and agentic AI
Develop reusable reference architectures, design patterns, standards, technical guardrails, and enterprise AI capabilities
Architect AI solutions involving model selection and routing, retrieval and grounding, context engineering, agent orchestration, tool use, state and memory, human-in-the-loop workflows, evaluation, observability, and secure enterprise integration
Define architecture patterns for AI/ML data pipelines, model serving, model lifecycle management, MLOps, deployment, monitoring, and operations across cloud, hybrid, on-premises, and restricted environments
Evaluate technology and platform alternatives and lead build, buy, configure, and integrate decisions
Lead architecture and technical design reviews
Partner with enterprise architecture, cybersecurity, data, identity, privacy, Responsible AI, business, product, engineering, and technology teams
Provide technical leadership and mentorship across engineering teams

Requirements

What you’ll need

A University Degree in Computer Science, Artificial Intelligence, Machine Learning, Engineering, or a related STEM discipline and a minimum of 10 years of relevant professional experience, or an Advanced Degree in a related field and a minimum of 7 years of relevant professional experience
A minimum of 5 years of experience designing, developing, integrating, or architecting AI/ML solutions, including taking AI or ML capabilities beyond experimentation into production environments
Experience serving as a technical architect, solution architect, technical lead, or senior engineer for complex enterprise software, data, cloud, or AI/ML systems
Technical experience with AI/ML systems and modern AI application architectures, including Generative AI and large language models
Experience designing distributed systems, APIs, microservices, enterprise integrations, data pipelines, or cloud-native applications using at least one major public cloud platform
Experience translating business and technical requirements into architecture designs and evaluating technical, business, cost, security, and operational tradeoffs
Experience with enterprise security concepts including identity and access management, authentication and authorization, data protection, application security, and secure system integration
U.S. Person requirement: lawful permanent resident, protected individual, U.S. citizen, U.S. national, refugee, or asylee status
Security clearance not required
Preferred: production Generative AI, retrieval-augmented generation, agentic AI, or multi-agent systems experience
Preferred: Model Context Protocol, agent identity, secure tool integration, AI evaluation, observability, tracing, guardrails, model monitoring, Responsible AI, model governance, MLOps, Kubernetes, containers, CI/CD, infrastructure-as-code, NIST AI RMF, TOGAF, Zachman, or similar frameworks
Ability to lead technical discussions, influence architecture decisions, and communicate complex technical concepts to technical and non-technical stakeholders

Benefits

Comp & perks

Compensation, healthcare, wellness, retirement and work/life benefits
Parental (including paternal) leave
Flexible work schedules
Achievement awards
Educational assistance
Child/adult backup care
Medical, dental and vision insurance
Life insurance
Short-term disability
Long-term disability
401(k) match
Flexible spending accounts
Employee assistance program
Employee Scholar Program
Paid time off
Holidays
Annual short-term and/or long-term incentive compensation programs (eligibility dependent on position and collective-bargaining coverage)