Architect, Enterprise AI

How do I make an impact on my team? • Define and maintain the enterprise AI architecture strategy, including standards and reference architectures that support scalable adoption of AI, generative AI, and agentic AI capabilities across the organization. • Establish and maintain enterprise AI architecture patterns, including design standards for generative AI, agentic workflows, retrieval-augmented generation (RAG), model orchestration, and enterprise AI service integration. • Develop and implement AI governance frameworks and architectural guardrails to ensure responsible, secure, and compliant use of AI technologies across the enterprise. • Define AI platform strategy by evaluating and recommending AI platforms, large language model providers, orchestration frameworks, and supporting tooling aligned with enterprise architecture standards. • Establish AI integration patterns that enable AI capabilities to integrate with enterprise platforms, APIs, data platforms, and cloud services while maintaining scalability and reliability. • Define and promote Agentic AI architectural patterns, including standards for workflow orchestration, tool integration, memory/context management, and human-in-the-loop capabilities. • Ensure adherence to Responsible AI and regulatory compliance requirements, including standards for data privacy, auditability, model transparency, and ethical AI practices. • Define architectural standards for AI operations (LLMOps/MLOps) including model lifecycle management, monitoring, observability, deployment pipelines, and operational controls. • Establish AI security and cost management practices, including secure model access, data protection standards, usage controls, and architectural patterns that optimize AI infrastructure and consumption costs. • Other duties as assigned.

What our team expects from you? • Bachelor’s degree in Computer Science, Information Systems, Software Engineering, or a related field, or equivalent work experience, required. • Certification/Licenses: • AWS Certified Solutions Architect Professional and AWS Certified Machine Learning – Specialty certification preferred. • Responsible AI / AI Governance certification preferred. • Industry-recognized Certified AI Professional certification or equivalent AI/ML certification preferred. • 10+ years of experience in software development or architecture, including a minimum of 5 years focused on Artificial Intelligence and Machine Learning technologies, required. • Demonstrated experience defining enterprise AI architecture patterns, including model integration, orchestration frameworks, and AI-enabled application design within cloud or distributed systems required. • Experience establishing or supporting AI governance frameworks, including responsible AI practices, regulatory compliance, model lifecycle oversight, and enterprise guardrails for safe AI adoption required. • Experience defining AI platform strategies, including evaluation, selection, and architectural guidance for enterprise AI platforms, large language models, and supporting tooling required. • Proven experience designing AI integration patterns that connect AI capabilities with enterprise platforms, APIs, data platforms, and cloud services required. • Experience implementing or guiding AI operations practices (MLOps/LLMOps) including model deployment, monitoring, lifecycle management, and operational governance for AI-enabled systems required. • Demonstrated experience addressing AI security, privacy, and cost management considerations, including secure model access, protection of sensitive data, and architectural approaches to optimize AI usage and operational costs required. • Experience collaborating with enterprise architecture, engineering, data, and product teams to enable enterprise adoption of AI capabilities and guide solution architecture decisions required. • Experience with interviewing end-users for insight on functionality, interface, problems, and/or usability issues required. • Advanced development skills in multiple modern languages, including C#, React, and Python required. • Knowledge of PBM systems, claims adjudication processes, and data exchange patterns between payers, providers, and pharmacies preferred. • Participate in, adhere to, and support compliance program objecti

Company Navitus About Us Navitus - Putting People First in Pharmacy - Navitus was founded as an alternative to traditional pharmacy benefit manager (PBM) models. We are committed to removing cost from the drug supply chain to make medications more affordable for the people who need them. At Navitus, our team members work in an environment that celebrates diversity, fosters creativity and encourages growth. We welcome new ideas and share a passion for excellent service to our customers and each other.. Current associates must use SSO login option at to be considered for internal opportunities. We are committed to providing equal employment opportunity to all applicants and employees and comply with all applicable nondiscrimination regulations, including those related to protected veterans and individuals with disabilities. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, or handicap. Pay Range USD $124,140.15 - USD $170,000.00 /Yr. STAR Bonus % (At Risk Maximum) 5.00 - Salaried Non-Management except pharmacists Work Schedule Description (e.g. M-F 8am to 5pm) M-F: 8:00 am – 5:00 pm Remote Work Notification ATTENTION: Navitus is unable to offer remote work to residents of Alaska, Hawaii, Maine, Mississippi, New Hampshire, New Mexico, North Dakota, Rhode Island, South Carolina, South Dakota, West Virginia, and Wyoming. Overview Due to growth, we are adding a Architect, Enterprise AI to our team! The Architect, Enterprise AI is responsible for designing, governing, and advancing the enterprise architecture strategy for Artificial Intelligence and Agentic AI capabilities across the organization. This role partners with Enterprise Architecture, Platform Engineering, Data Engineering, and Product teams to define scalable AI architecture patterns, reference architectures, and governance frameworks that enable responsible and secure use of AI technologies. The Architect serves as the enterprise subject matter expert on AI platforms, generative AI, and agentic workflows, ensuring alignment with enterprise architecture principles, security requirements, and regulatory standards while enabling innovation and business value. Is this you? Find out more below! Responsibilities How do I make an impact on my team? • Define and maintain the enterprise AI architecture strategy, including standards and reference architectures that support scalable adoption of AI, generative AI, and agentic AI capabilities across the organization. • Establish and maintain enterprise AI architecture patterns, including design standards for generative AI, agentic workflows, retrieval-augmented generation (RAG), model orchestration, and enterprise AI service integration. • Develop and implement AI governance frameworks and architectural guardrails to ensure responsible, secure, and compliant use of AI technologies across the enterprise. • Define AI platform strategy by evaluating and recommending AI platforms, large language model providers, orchestration frameworks, and supporting tooling aligned with enterprise architecture standards. • Establish AI integration patterns that enable AI capabilities to integrate with enterprise platforms, APIs, data platforms, and cloud services while maintaining scalability and reliability. • Define and promote Agentic AI architectural patterns, including standards for workflow orchestration, tool integration, memory/context management, and human-in-the-loop capabilities. • Ensure adherence to Responsible AI and regulatory compliance requirements, including standards for data privacy, auditability, model transparency, and ethical AI practices. • Define architectural standards for AI operations (LLMOps/MLOps) including model lifecycle management, monitoring, observability, deployment pipelines, and operational controls. • Establish AI security and cost management practices, including secure model access, data protection standards, usage controls, and architectural patterns that optimize AI infrastructure and consumption costs. • Other duties as assigned. Qualifications What our team expects from you? • Bachelor’s degree in Computer Science, Information Systems, Software Engineering, or a related field, or equivalent work experience, required. • Certification/Licenses: • AWS Certified Solutions Architect Professional and AWS Certified Machine Learning – Specialty certification preferred. • Responsible AI / AI Governance certification preferred. • Industry-recognized Certified AI Professional certification or equivalent AI/ML certification preferred. • 10+ years of experience in software development or architecture, including a minimum of 5 years focused on Artificial Intelligence and Machine Learning technologies, required. • Demonstrated experience defining enterprise AI architecture patterns, including model integration, orchestration frameworks, and AI-enabled application design within cloud or distributed systems required. • Experience establishing or supporting AI governance frameworks, including responsible AI practices, regulatory compliance, model lifecycle oversight, and enterprise guardrails for safe AI adoption required. • Experience defining AI platform strategies, including evaluation, selection, and architectural guidance for enterprise AI platforms, large language models, and supporting tooling required. • Proven experience designing AI integration patterns that connect AI capabilities with enterprise platforms, APIs, data platforms, and cloud services required. • Experience implementing or guiding AI operations practices (MLOps/LLMOps) including model deployment, monitoring, lifecycle management, and operational governance for AI-enabled systems required. • Demonstrated experience addressing AI security, privacy, and cost management considerations, including secure model access, protection of sensitive data, and architectural approaches to optimize AI usage and operational