Core Agent Engineer

Adobe

. Own full-stack implementation across Adobe's Agentic Product, including LLM integration, agentic reasoning layers, APIs, client runtimes, and desktop, mobile, and web delivery .

Posted 10/15/2026full-timeUnited StatesMid-LevelSenior💰 $159,200 - $301,600 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in full-stack engineering with a focus on large language models, agentic systems, and AI-assisted engineering practices. Proficient in collaborating cross-functionally to deliver robust, scalable, and maintainable production systems while adhering to quality and responsible AI standards.

Highest-signal resume keywords

Large Language ModelsAgentic Systems DevelopmentAI-Assisted Engineering ToolsFull-Stack EngineeringCross-Functional Collaboration

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

LLM APIsPrompt EngineeringContext Window ManagementMulti-Step ReasoningOrchestration FrameworksMemory PersistenceProduction-Quality CodeTestingDeveloper ScaffoldingTool Use

Soft Skills

Clear CommunicationAdaptability

Tools & Technologies

Claude CodeCodexCursorCI/CDLocal Development Tools

Industry Keywords

Generative AIResponsible AIQuality AssuranceTrust and Reliability

About the role

Key responsibilities & impact

Own full-stack implementation across Adobe's Agentic Product, including LLM integration, agentic reasoning layers, APIs, client runtimes, and desktop, mobile, and web delivery
Translate prototype capabilities into robust, scalable, maintainable production systems
Implement model orchestration, tool use, multi-step task execution, context and memory management, and agent evaluation infrastructure
Champion AI-assisted engineering practices using Claude Code, Codex, and emerging agentic development platforms
Improve developer experience through tooling, local development loops, CI/CD ergonomics, and team scaffolding
Evaluate new LLM capabilities and frameworks and recommend adoption strategies
Build and maintain evaluation frameworks, benchmarks, and feedback loops for agent behavior
Partner with Engineering and Product leadership to translate product ambitions into technically grounded delivery
Collaborate with platform, infrastructure, data, and security teams on guardrails, data protection, observability, and operational requirements
Raise engineering standards through excellent code, high-signal code reviews, and technical knowledge sharing

Requirements

What you’ll need

Solid, hands-on experience with large language models, including LLM APIs, prompt engineering, context window management, retrieval-augmented generation, and operating LLM-backed systems in production
Experience building and shipping agentic systems, including tool use, multi-step reasoning, orchestration frameworks, memory and context persistence, and agent failure modes
Hands-on experience with AI-assisted engineering tools such as Claude Code, Codex, Cursor, or similar
Full-stack engineering experience across backend services, client SDKs, and platform-specific integrations
Strong engineering craft, including readable production-quality code, testing, local development ergonomics, and developer scaffolding
Clear communication skills for discussing technical tradeoffs with product managers and senior technical stakeholders
Ability to operate in a fast-moving, ambiguous environment
Experience collaborating cross-functionally with product, design, platform, and security teams on user-facing AI products
Familiarity with quality, trust, reliability, and responsible AI requirements for generative and agentic systems

Benefits

Comp & perks

Annual Incentive Plan (AIP)
Potential new hire equity award
Comprehensive benefits programs
Accessible recruiting process and reasonable accommodations
Equal Employment Opportunity protections