AI Architect

OverviewYour Future. Secured. ISC2 is a force for good. As the world’s leading nonprofit member organization for cybersecurity professionals, our core values — Integrity, Advocacy, Commitment, Inclusion, and Excellence — drive everything we do in support of our vision of a safe and secure cyber world. Our globally recognized, award-winning portfolio of certifications provide an independent and globally recognized endorsement of cybersecurity knowledge, skills and experience for all career levels. Our charitable arm, the Center for Cyber Safety and Education, enables ISC2 and our members to serve the public by educating the most vulnerable about cyber risks and empowering access to enter and thrive in the cyber profession. Learn more at ISC2 online and connect with us on Twitter, Facebook and LinkedIn. When you join ISC2, you’ll demonstrate your commitment to an inclusive and equitable environment. Your support of the unique perspectives and experiences shared by our global cybersecurity workforce and profession will be recognized. We invite you to take an active role in helping us create a true sense of belonging across our organization — an environment of authenticity, trust, empowerment and connectedness that empowers all of our successes. Learn more.Position SummaryThe 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 role is responsible for defining and documenting AI use cases and target delivery models, establishing the 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**.ResponsibilitiesDefine and document enterprise AI use cases, business value drivers, and target delivery models that align with organizational goals and objectives. Develop and maintain current-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 platforms and services, and provide appropriate recommendations. Establish reusable AI architecture patterns, reference models, and implementation standards to support consistent delivery. Guide proof-of-concept and pilot strategies to validate solution 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 with architecture, 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.QualificationsDeep 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-on proficiency with Python and familiarity with common AI/ML frameworks and tooling such as PyTorch, TensorFlow, scikit-learn, LangChain or Semantic Kernel, APIs, and vector databases. Demonstrated expertise in MLOps/LLMOps practices, 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, and establish reusable 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.St