Full Stack Software Architect - Agentic AI

Adobe Systems Incorporated•Lehi, UT

52d

About The Position

Changing the world through digital experiences is what Adobe's all about. We give everyone-from emerging artists to global brands-everything they need to design and deliver exceptional digital experiences! We're passionate about empowering people to create beautiful and powerful images, videos, and apps, and transform how companies interact with customers across every screen. We're on a mission to hire the very best and are committed to creating exceptional employee experiences where everyone is respected and has access to equal opportunity. We realize that new ideas can come from everywhere in the organization, and we know the next big idea could be yours! Adobe is looking for a Full Stack Software Architect to join a fast-moving team focused on rapidly prototyping new product ideas and exploring emerging opportunities in AI-powered experiences. This team operates at the intersection of data, visualization, and AI, building interactive experiences that help users understand and act on insights in new ways. You'll work closely with designers, product managers, and product strategists to develop intelligent prototypes and systems that showcase the next generation of AI-enabled Adobe experiences. From backend architecture to LLM orchestration, you'll help shape how generative AI becomes a seamless part of Adobe's ecosystem.

Requirements

Bachelor's degree in Computer Science, Information Technology, or a related field - or equivalent practical experience.
8+ years of professional experience developing full-stack web applications.
Strong proficiency with JavaScript/TypeScript, React, and Node.js (or similar frameworks).
Proven experience architecting and deploying AI or LLM-powered applications using frameworks like LangChain, LangGraph, or equivalent.
Deep understanding of AI agent orchestration, tool calling, MCPs, and AI-focused backend architectures.
Experience designing resilient, scalable backend services that integrate LLMs, APIs, and data sources.
Experience using LangSmith or similar tools for LLM testing, evaluation, and monitoring.
Strong understanding of prompt engineering, context management, retrieval pipelines, and evaluation methodologies.
A collaborative mindset and a passion for rapid experimentation and discovery.
Familiarity with cloud platforms such as AWS, Azure, or GCP.

Nice To Haves

Experience designing multi-agent systems or integrating custom tools and APIs into agentic workflows.
Familiarity with vector databases, retrieval-augmented generation (RAG), or knowledge graph architectures.
Experience developing AI evaluation frameworks or LLM observability pipelines.
Understanding of AI ethics, privacy, and responsible AI principles.
Previous experience driving architectural direction for AI-first products or large-scale distributed systems.
Passion for data visualization and data storytelling.
Familiarity with data visualization libraries such as Vega, Vega-Lite, and React Spectrum Charts.

Responsibilities

Design, build, and deploy AI-powered full-stack prototypes and applications that explore new product directions.
Use frameworks like LangChain and LangGraph to develop agentic workflows and multi-step reasoning systems powered by LLMs.
Architect and implement tool-based AI systems, including tool calling, custom tool creation, and multi-agent coordination.
Integrate LLMs and generative AI APIs (e.g., OpenAI, Anthropic, Vertex AI) into interactive applications that enhance creativity and user experience.
Use LangSmith or similar platforms to test, evaluate, and monitor LLM performance, reliability, and quality.
Collaborate with designers, PMs, and researchers to prototype rapidly and validate new ideas.
Build and maintain robust backend services and APIs that power AI-driven experiences.
Stay current with emerging AI frameworks, model capabilities, and evaluation techniques to identify new opportunities for innovation.
Establish and uphold strong engineering practices for testing, observability, and continuous integration, even in fast-paced prototype environments.

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