Senior AI Architect - Agent Learning & Evaluation

Adobe; San Jose (CA); On-site; USD 229,000 - 331,000; Full time

Job summary

Adobe is seeking a Senior AI Architect to define and lead the architecture for Commerce Factory’s Agent Harness and Learning Layer. You will shape how agents learn, evaluate results, and improve from release to release, collaborating with engineers, product leaders, and platform teams.

This role focuses on building scalable, production-grade AI systems, applying reinforcement learning and preference modeling, and setting technical direction across the platform.

Qualifications

• 12+ years of software engineering, including AI/ML at scale.
• Experience crafting LLM and agentic systems with tool use and context management.
• Experience with reinforcement learning, preference learning, reward modeling, or RLHF.
• Ability to translate ambiguous objectives into architecture and strategy.
• Experience applying AI/ML to products with measurable improvements.
• Ability to lead through architecture reviews and mentoring.
• Proficiency in Python and at least one other language.
• Experience with AWS or Azure and ML pipelines.
• Excellent collaboration across engineering, product, and platform teams.

Responsibilities

• Define architecture for agent learning and feedback systems.
• Design pipelines for evaluating agent outcomes and signals.
• Apply AI/ML techniques like preference learning and RL.
• Define Learning Layer integration with Harness, evaluation, skills, tool calling.
• Establish architecture standards across Commerce Factory.
• Develop prototypes and assess technologies for production capabilities.
• Mentor engineers and contribute to technical development.
• Partner with product and platform teams to map roadmap.

Skills

Software engineering

LLM & agent systems

Reinforcement learning

Python

Cloud platforms

Cross-team collaboration

Job description

Adobe is seeking a Senior AI Architect to define and lead the architecture for Commerce Factory’s Agent Harness and Learning Layer. You will shape how agents learn, evaluate results, and improve from release to release, collaborating with engineers, product leaders, and platform teams.

This role focuses on building scalable, production-grade AI systems, applying reinforcement learning and preference modeling, and setting technical direction across the platform.