Enterprise AI Architecture CTH after 12 months Responsibilities: Enterprise AI Architecture: • Design and maintain Client's enterprise AI platform architecture • Establish technical standards for AI solution implementation • Define integration patterns for AI services across Client's technology landscape • Create and maintain AI reference architectures, including RAG implementations • Develop model registry and lifecycle management frameworks Technical Leadership: • Lead the technical design of AI platform components and services • Define agent orchestration standards and patterns • Establish guardrails for AI implementation and deployment • Create architectural patterns for knowledge base governance • Guide teams on technical implementation of AI solutions Standards & Governance: • Develop technical standards for AI model deployment and management • Create governance frameworks for AI platform components • Define security and compliance requirements for AI implementations • Establish monitoring and observability standards • Maintain architectural compliance across AI initiatives Solution Design & Review: • Review and approve AI solution designs • Ensure alignment with enterprise architecture standards • Identify opportunities for reusable components • Guide teams on technical implementation approaches • Evaluate new AI technologies and platforms Platform Evolution: • Drive the evolution of Client's AI platform capabilities • Identify and evaluate emerging AI technologies • Define roadmap for platform capabilities and features • Ensure scalability and performance of AI solutions • Guide integration with existing enterprise systems Requirements: • 10+ years of enterprise architecture experience • 5+ years specifically focused on AI/ML platforms and solutions • Strong background in large-scale distributed systems • Experience with major cloud AI services and platforms • Deep understanding of AI security and governance requirements • Expert knowledge of AI/ML architectures and platforms • Strong enterprise architecture capabilities • Deep understanding of security and compliance requirements • Experience with cloud-native architectures • Knowledge of AI model lifecycle management • Expertise in data architecture and integration patterns • Strong technical leadership and communication abilities • Large Language Models and GenAI technologies • RAG architectures and implementation patterns • Cloud AI services (AWS, Azure, GCP) • Enterprise integration patterns • Security and compliance frameworks • Data architecture and governance • Model operations and MLOps
