Principal Architect, Express AI Foundations
Adobe · San Jose, CA · Engineering and Product
About this role
Adobe is hiring a principal-level Network Engineer in the operations function based in San Jose, CA. The posting calls out experience with Python, Java, Express, Kafka. Compensation is listed at $206,400–$379,100 per year.
- Role
- Network Engineer
- Function
- operations
- Level
- principal
- Track
- Tech leadership
- Employment
- Full-time
- Location
- San Jose, CA
- Department
- Engineering and Product
- Posted
- Mar 25, 2026
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Job description
from Adobe careersThe 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 cohesive architecture.
- Develop large-scale data and inference infrastructure to support model training, fine-tuning, evaluation, and deployment — employing Spark, Kafka, Flink, and other distributed frameworks.