AI Agent Engineer

Adobe•San Jose, CA

About The Position

Our Company 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! The Opportunity We are looking for a hands-on, systems-oriented AI Agent Engineer to design, build, and maintain intelligent agents that drive automation and business impact across the enterprise. This role is responsible for the full lifecycle of agent development — from design to versioning, orchestration, and continuous learning. You’ll contribute directly to scaling our AI strategy by engineering reusable components, optimizing agent workflows, and ensuring real-world performance in production environments. What you'll Do Agent Development Build and fine-tune specialized AI agents for targeted customer experience use cases such as discovery, support, and lead qualification Implement prompt engineering strategies, memory handling, resource management and tool-calling integrations Multi-Agent Communication Adopt agent-to-agent communication protocols and handoff mechanisms to enable cooperative task execution and delegation Build orchestrated workflows across agents using frameworks like LangChain, AutoGen, or Semantic Kernel Templates & Reusability Create reusable agent templates and modular components to accelerate deployment across business units Build plug-and-play configurations for domain-specific requirements Lifecycle Management & Monitoring Track and improve conversation quality, task success rate, user satisfaction, and performance metrics Automate monitoring of agent behavior using observability tools (e.g., Arize, LangSmith, custom dashboards) Continuous Improvement Implement learning workflows, including human-in-the-loop feedback and automatic retraining Refine prompts and model behavior through structured experimentation and feedback loops Maintenance & Governance Handle knowledge base updates, drift detection, performance degradation, and integration of new business logic Ensure agents stay aligned with evolving enterprise data sources and compliance requirements Deployment Manage agent versioning, testing pipelines (unit, regression, UX), and controlled rollout processes Collaborate with DevOps, QA, and infrastructure teams to ensure scalable deployments

Requirements

3–5+ years of experience in AI/ML engineering, NLP systems, or backend development
Strong proficiency with LLM frameworks (e.g., OpenAI APIs, LangChain, RAG pipelines)
Experience building conversational agents or workflow bots in production environments
Familiarity with cloud platforms (AWS/GCP/Azure), REST APIs, Python, and containerization (Docker, K8s)
Comfort with prompt design, vector databases, and memory handling strategies

Nice To Haves

Experience with multi-agent frameworks or agent orchestration systems
Familiarity with observability tools, data labeling workflows, or synthetic data generation
Background in conversational design or dialogue management systems
Degree in Computer Science, Data Science, Engineering, or a related field

Responsibilities

Build and fine-tune specialized AI agents for targeted customer experience use cases such as discovery, support, and lead qualification
Implement prompt engineering strategies, memory handling, resource management and tool-calling integrations
Adopt agent-to-agent communication protocols and handoff mechanisms to enable cooperative task execution and delegation
Build orchestrated workflows across agents using frameworks like LangChain, AutoGen, or Semantic Kernel
Create reusable agent templates and modular components to accelerate deployment across business units
Build plug-and-play configurations for domain-specific requirements
Track and improve conversation quality, task success rate, user satisfaction, and performance metrics
Automate monitoring of agent behavior using observability tools (e.g., Arize, LangSmith, custom dashboards)
Implement learning workflows, including human-in-the-loop feedback and automatic retraining
Refine prompts and model behavior through structured experimentation and feedback loops
Handle knowledge base updates, drift detection, performance degradation, and integration of new business logic
Ensure agents stay aligned with evolving enterprise data sources and compliance requirements
Manage agent versioning, testing pipelines (unit, regression, UX), and controlled rollout processes
Collaborate with DevOps, QA, and infrastructure teams to ensure scalable deployments