Senior AI Platform Engineer

Adobe; San Jose (CA); On-site; USD 140,000 - 180,000; Full time

Job summary

Adobe is seeking a Staff Engineer – AI for Engineering to architect and evolve its AI platform. The ideal candidate will have over 10 years of experience in building large-scale distributed systems and a deep understanding of LLMs.

Your responsibilities include designing systems for coordinating LLMs, developing agent frameworks, and mentoring engineers. Proficiency in TypeScript, Python, and understanding of AI architectures are essential.

Qualifications

• 10+ years of experience building large-scale distributed systems, AI platforms, or intelligent service architectures.
• Deep understanding of how LLMs behave in production environments.
• Strong experience building AI-powered systems using LLM orchestration frameworks.

Responsibilities

• Architect and evolve the AI platform powering Adobe Engineering.
• Design and implement scalable orchestration layers that coordinate LLMs, tools, APIs.
• Build production-grade agent frameworks supporting multi-agent collaboration.

Skills

Large-scale distributed systems

AI platforms

LLM orchestration frameworks

TypeScript

Python

Java

Job description

Overview

Adobe empowers individuals and organizations to create exceptional content effortlessly. The AI for Engineering team builds a scalable, production‑grade AI platform that powers creativity across design, imaging, motion, and personalization. We are seeking a Staff Engineer – AI for Engineering to design and build the next‑generation AI systems that bring intelligent, adaptive, and agentic experiences to Adobe Express. This role is ideal for an engineer who deeply understands how modern LLMs and generative models behave in real‑world systems—including reasoning patterns, tool use, prompt dynamics, failure modes, and orchestration strategies—and can translate that understanding into scalable, production‑ready platforms.

Responsibilities

• Architect and evolve the AI platform powering Adobe Engineering—with a strong emphasis on Agentic AI systems and LLM‑native architectures.
• Design and implement scalable orchestration layers that coordinate LLMs, tools, APIs, memory stores, and multi‑step reasoning workflows.
• Build production‑grade agent frameworks that support planning, task decomposition, tool invocation, multi‑agent collaboration, and persistent memory.
• Develop high‑performance inference and runtime systems with strong guarantees around latency, reliability, observability, and cost efficiency.
• Design evaluation and feedback systems that measure reasoning quality, task success, hallucination rates, and agent behavior—enabling rapid iteration and continuous improvement.
• Integrate first‑party and third‑party foundation models into cohesive, adaptive systems using routing, model selection, guardrails, and fallback strategies.
• Design data flows, session‑level intelligence, and contextual memory systems that allow agents to operate coherently across interactions.
• Partner with applied research, product, and platform teams to bring intelligent agentic capabilities into real developer‑facing experiences.
• Drive architectural strategy for AI for Engineering—connecting models, reasoning engines, tools, and data streams into adaptive AI systems.
• Mentor senior engineers in modern AI system design, LLM orchestration patterns, and agent platform architecture.

Qualifications

• 10+ years of experience building large‑scale distributed systems, AI platforms, or intelligent service architectures.
• Deep understanding of how LLMs behave in production environments—including prompting strategies, reasoning chains, tool usage, grounding techniques, hallucination mitigation, guardrails, and evaluation patterns.
• Strong experience building AI‑powered systems using LLM orchestration frameworks, model routing strategies, and multi‑model pipelines.
• Hands‑on experience designing agentic systems—including reasoning loops, memory persistence, tool integration, state management, and multi‑agent coordination.
• Proven expertise in building scalable, cloud‑native, microservices‑based architectures with strong observability and reliability.
• Experience designing evaluation systems for generative AI quality, task completion, and behavioral robustness.
• Proficiency in TypeScript, Python and at least one systems language (Java, Go, C++), with experience building production AI services.
• Strong systems thinking—ability to connect model capabilities, runtime constraints, and product requirements into coherent architectures.
• Excellent multi‑functional communication skills, with experience influencing architectural direction across research and engineering teams.

Preferred Qualifications

• Experience architecting AI assistants, copilots, or agent platforms in production environments.
• Experience working with multimodal generative systems (text, image, video, motion).
• Familiarity with tool‑augmented LLM systems, RAG architectures, vector databases, and contextual memory systems.
• Exposure to evaluation frameworks for generative AI quality and safety.
• Experience contributing to open‑source AI frameworks or publishing technical thought leadership.

Equal Employment Opportunity Statement

Adobe is proud to be an Equal Employment Opportunity employer. We do not discriminate based on gender, race or color, ethnicity or national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status, or any other protected characteristic.