Enterprise AI Architect

As an Enterprise AI Architect, you will serve as the primary technical authority for designing and commercializing end-to-end AI ecosystems. This role focuses on bridging the gap between core infrastructure and the high-level AI Knowledge Layer, ensuring that models, data pipelines, and applications are integrated into repeatable, globally deliverable services. You will lead the architectural strategy for shifting from traditional storage and compute to sophisticated Model AI (MaaS) and Agentic AI frameworks. Key Responsibilities • Full-Stack AI Architecture Design: • Architect comprehensive AI platforms spanning training, inference, and data pipelines across core, cloud, and edge environments. • Design the AI Knowledge Layer, integrating object storage, vector databases, and lakehouse architectures to support RAG (Retrieval-Augmented Generation) and enterprise search. • Define reference architectures for Model-as-a-Service (MaaS) and the placement of AI workloads based on latency, resilience, and scale. • Solution Engineering & Service Blueprints: • Translate complex AI platform capabilities into customer-ready solutions and deployable service blueprints. • Collaborate with infrastructure and network engineers to ensure AI platforms align with existing enterprise operational and delivery models. • Develop application-level AI frameworks, guiding the integration of LLMs into functional enterprise software and workflows. • Technical GTM & Strategic Leadership: • Act as the architectural anchor for regional GTM initiatives, translating deep technical AI capabilities into clear value propositions for enterprise customers. • Guide specialist engineering teams toward integrated outcomes, enabling them to execute confidently in new technology domains like GenAI. • Support high-stakes enterprise pursuits by aligning technical intent with commercial viability and long-term scalability. Required Qualifications • Architectural Expertise: • Proven experience in Data & AI Platform Architecture, including deep knowledge of distributed systems and modern data stacks. • Significant background in architecting for performance, resilience, and scalability within global service provider or large enterprise environments. • AI/ML Domain Knowledge: • Advanced understanding of AI/ML infrastructure, including experience with leading models from OpenAI, Anthropic, or Google. • Expertise in database technologies (e.g., MongoDB, Snowflake) as foundational components for AI solution stacks. • Strategic Regional Focus: • Extensive professional history within the APAC region, managing complex stakeholder environments and cross-functional teams. • A track record of driving technical strategy for high-growth business units in the cloud and data sectors.