Principal Architect, Express AI Foundations

The Opportunity Adobe Express enables all users, whether individuals or large organizations, to effortlessly produce impressive content. The AI Foundations team constructs a flexible, scalable AI framework that drives creativity at scale in design, imaging, motion, and personalization. We're looking for a Principal Architect to build and implement the AI framework for Adobe Express, merging strong ML skills with proficiency in distributed systems, data architecture, and large-scale service development. You'll define and invent the end-to-end foundation that brings Agentic AI, Create AI, Imaging AI, Motion AI, and Personalization AI to life — spanning model orchestration, inference systems, data pipelines, caching and storage layers, session analytics, and continuous evaluation frameworks. This role blends applied research and engineering leadership — ideal for someone who can connect modeling innovation with production-grade systems that deliver real-time, customer-facing intelligence at scale. What You’ll Do Architect and evolve the complete AI stack for Adobe Express — covering Agentic AI, Construct AI, Imaging AI, Motion AI, and Personalization AI. Develop and operationalize end-to-end systems — integrating microservices, data pipelines, LLM orchestration layers, in-house and third-party models, databases, caches, session analytics, and evaluation systems into a cohes [... source excerpt omitted ...] feedback loops, and quality metrics that drive continuous improvement. Work closely with applied research, product, and platform teams to implement LLMs and other AI models into customer-facing services. Drive architectural strategy for Express AI Foundations — connecting ML models, reasoning engines, and data streams into adaptive, intelligent systems. Mentor senior engineers and scientists, encouraging excellence across architecture, experimentation, and AI system composition. What You’ll Bring 10+ years of experience in large-scale distributed systems AI infrastructure, or ML platform engineering. Deep understanding of ML and LLM fundamentals — training, fine-tuning, de [... source excerpt omitted ...] oops, memory persistence, task decomposition, and multi-agent coordination. Skill to merge engineering precision with practical research understanding, connecting prototyping and production. Strong communication and collaboration skills, with experience influencing cross-functional technical direction. Preferred Qualifications Bachelor's or equivalent experience in Computer Science, Data Science, Machine Learning, or a related technical field. Experience architecting AI assistants, build agents, or multimodal creative systems. Exposure to Generative AI (LLMs, diffusion, or multimodal architectures). Experience with MLOps pipelines, feature stores, and model registries. Trac