Overview

Position Summary

The AI Architect will provide strategic and technical leadership for the organization’s AI ecosystem by translating business priorities into scalable, secure, and governed solution designs. This roleis responsible fordefining and documenting AI use cases and target delivery models,establishingthe current and future-state AI architecture across the enterprise landscape, evaluating build-versus-buy options, and guiding proof-of-concept strategies and reusable implementation patterns. The position also drives operational maturity by embedding monitoring,feasibility of telemetry across the AI landscape,observability, governance, and change management practices that enable AI capabilities to scale effectively across delivery teams while supporting reliable, compliant, and business-aligned adoption.

**This position is not available to residents of California**.

Responsibilities

• Define and document enterprise AI use cases, business value drivers, and target delivery models that align with organizational goals andobjectives.
• Develop andmaintaincurrent-state and target-state AI architecture across the enterprise, including platforms, data flows, integration patterns, security controls, and governance requirements.
• Lead build-versus-buy evaluations for AI platformsand services, and provide appropriate recommendations.
• Establish reusable AI architecture patterns, reference models, and implementation standards to support consistent delivery.
• Guideproof-of-concept and pilot strategies tovalidatesolution feasibility, technical design, business value, and operational readiness before broader adoption.
• Drive scalable AI operations by embedding monitoring, telemetry, observability, governance, and change management practices across the AI lifecycle.
• Partner witharchitecture,engineering, data, security, product, and business stakeholders to translate requirements into secure, reliable, and governed AI solutions.
• Define key deliverables for the function, including AI architecture models, proof-of-concept strategies, and delivery patterns that accelerate enterprise adoption.
• Perform miscellaneous duties, as required.

Behavioral Competencies

• Ability to demonstrate and support the 5 Company Core Values: Integrity, Advocacy, Commitment, Inclusion, and Excellence.

Qualifications

• Deep knowledge of AI/ML concepts and patterns, including machine learning, generative AI, large language models (LLMs), prompt design, retrieval-augmented generation (RAG), model evaluation, and responsible AI practices.
• Hands-onproficiencywith Python and familiarity with common AI/ML frameworks and tooling such asPyTorch, TensorFlow, scikit-learn,LangChainor Semantic Kernel, APIs, and vector databases.
• DemonstratedexpertiseinMLOps/LLMOpspractices, including CI/CD, model deployment, observability, telemetry, drift and performance monitoring, cost optimization, and lifecycle management.
• Strong understanding of AI security, privacy, governance, and compliance requirements, including access controls, data protection, auditability, risk management, bias mitigation, and human-in-the-loop controls.
• Relevant cloud, architecture, or AI certifications are preferred (for example, Azure AI Engineer, AWS Machine Learning, Google Professional Machine Learning Engineer, or equivalent architecture certifications).
• Proven ability to conduct build-versus-buy assessments, evaluate vendors and platforms, define reference architectures, andestablishreusable design patterns and standards.
• Excellent collaboration and communication skills, with the ability to translate business priorities into technical roadmaps, solution designs, proof-of-concept strategies, and executive-ready recommendations.
• Strong analytical andmanagementcapabilities, including facilitation of cross-functional teams, change management, and mentoring delivery teams on AI architecture best practices.

Education and Work Experience

• Bachelor’s degree in Computer Science, Information Systems, Data Science, Software Engineering, or a related technical fieldrequired;Master’sdegree preferred.
• 10+ years of progressive experience in enterprise architecture, software engineering, data/analytics, or AI/ML solution delivery, including hands-onrecentexperience designing and operationalizing AI solutions.
• Strong architecture experience across cloud and enterprise platforms, with the ability to design scalable, secure, and governed AI solutions using services in Azure, AWS, or Google Cloud.
• Experience with data architecture and engineering, including data pipelines, ETL/ELT, structured and unstructured data, feature or semantic layers, and integration patterns with enterprise applications and APIs.
• Experience leading enterprise architecture, platform modernization, digital transformation, or AI/ML initiatives in a complex business environment is strongly preferred.
• Experience working with cross-functional teams including engineering, data, security, product, legal/compliance, and business stakeholders.

Physical and Mental Demands

• Up to 10% travel may be required.
• Work normal business hours and extended hours when necessary.
• Occasionaleveningandweekendworktomeetdeadlines,deploychanges,ortest implementations.
• Availability for on-call needs.
• Remain in a stationary position, often standing or sitting, for prolonged periods.
• Regular use of office equipment in a remote environment such as a computer/laptop and monitor computer screens.
• Dexterity of hands and fingers to operate a computer keyboard, mouse, and other computer components.