AI Architecture Manager

Role Overview As an AI Architecture Manager, you will lead the design and evolution of enterprise AI platforms and architecture frameworks that enable scalable adoption of Artificial Intelligence across the organization. You will define the target-state AI architecture, evaluate emerging technologies, and establish secure, governed, and high-performing AI foundations that support Generative AI, Large Language Models (LLMs), and advanced AI workloads. Working closely with business and technology stakeholders, you will drive the implementation of enterprise AI capabilities that accelerate innovation while ensuring operational excellence and responsible AI practices. Key Responsibilities • Design and evolve enterprise AI architecture frameworks, including Generative AI, Large Language Models (LLMs), and multi-modal AI solutions. • Define target-state AI platform architectures and develop implementation roadmaps that support enterprise-wide AI adoption and scalability. • Evaluate emerging AI technologies, platforms, and architectural patterns, providing recommendations on technology selection and investment decisions. • Design and operationalize end-to-end AI foundations that support model development, deployment, monitoring, governance, and lifecycle management. • Assess and integrate technologies across data platforms, AI/ML ecosystems, cloud infrastructure, security controls, and governance frameworks. • Architect and develop custom platform components that address enterprise AI scalability, performance, and operational requirements. • Optimize compute infrastructure and model-serving environments to improve performance, availability, observability, and cost efficiency. • Establish AI platform standards, architectural principles, and best practices that support secure and scalable AI delivery. • Implement Responsible AI, security, compliance, risk management, and governance controls across AI platforms and solutions. • Collaborate with engineering, data, security, and business teams to ensure AI architecture aligns with enterprise objectives and regulatory requirements. • Provide technical leadership and guidance throughout AI transformation and modernization initiatives. Job Qualifications Basic Qualifications • Proven experience in AI Architecture, Enterprise Architecture, Cloud Architecture, Data Platforms, or related technology leadership roles. • Proven experience designing and implementing enterprise-scale AI and Machine Learning platforms. • Strong understanding of Generative AI, Large Language Models (LLMs), multi-modal AI models, and modern AI architecture patterns. • Experience with cloud platforms such as Microsoft Azure, AWS, or Google Cloud Platform and their AI/ML services. • Expertise in MLOps, AI platform engineering, model deployment, monitoring, and AI operations. • Experience designing governance, security, compliance, and Responsible AI frameworks for enterprise solutions. • Strong understanding of distributed systems, cloud-native architectures, and scalable infrastructure design. • Experience leading architecture decisions and influencing technology strategy across cross-functional teams. Preferred Qualifications • Experience building enterprise AI foundations, AI Centers of Excellence (CoE), or large-scale AI transformation programs. • Knowledge of Retrieval-Augmented Generation (RAG), vector databases, AI agents, orchestration frameworks, and advanced AI platform architectures. • Experience with Kubernetes, containerization, distributed computing, and high-performance AI infrastructure. • Expertise in AI observability, model performance optimization, platform reliability, and cost management. • Strong stakeholder management, consulting, and executive communication skills. • Relevant certifications in Cloud Architecture, Artificial Intelligence, Machine Learning, or Enterprise Architecture are preferred. Show more Show less

Basic Qualifications: Proven experience in AI Architecture, Enterprise Architecture, Cloud Architecture, Data Platforms, or related technology leadership roles. Proven experience designing and implementing enterprise-scale AI and Machine Learning platforms. Strong understanding of Generative AI, Large Language Models (LLMs), multi-modal AI models, and modern AI architecture patterns. Experience with cloud platforms such as Microsoft Azure, AWS, or Google Cloud Platform and their AI/ML services. Expertise in MLOps, AI platform engineering, model deployment, monitoring, and AI operations. Experience designing governance, security, compliance, and Responsible AI frameworks for enterprise solutions. Strong understanding of distributed systems, cloud-native architectures, and scalable infrastructure design. Experience leading architecture decisions and influencing technology strategy across cross-functional teams.

Design and evolve enterprise AI architecture frameworks, including Generative AI, Large Language Models (LLMs), and multi-modal AI solutions. Define target-state AI platform architectures and develop implementation roadmaps that support enterprise-wide AI adoption and scalability. Evaluate emerging AI technologies, platforms, and architectural patterns, providing recommendations on technology selection and investment decisions. Design and operationalize end-to-end AI foundations that support model development, deployment, monitoring, governance, and lifecycle management. Assess and integrate technologies across data platforms, AI/ML ecosystems, cloud infrastructure, security controls, and governance frameworks. Architect and develop custom platform components that address enterprise AI scalability, performance, and operational requirements. Optimize compute infrastructure and model-serving environments to improve performance, availability, observability, and cost efficiency. Establish AI platform standards, architectural principles, and best practices that support secure and scalable AI delivery. Implement Responsible AI, security, compliance, risk management, and governance controls across AI platforms and solutions. Collaborate with engineering, data, security, and business teams to ensure AI architecture aligns with enterprise objectives and regulatory requirements. Provide technical leadership and guidance throughout AI transformation and modernization initiatives.