Staff Agentic AI Engineer - Marketing

Role overview

This is a senior individual contributor position focused on building production-grade agentic AI systems that power marketing-oriented enterprise automation for large global brands. The role centers on expanding a no-code agent platform, with hands-on responsibility for architecture, evaluation, and optimization of LLM-driven agents in real-world deployments.

Responsibilities

- Design, build, and improve production AI agent systems that optimize web and chat experiences grounded in structured data. - Architect agent workflows involving reasoning, tool use, retrieval, guardrails, escalation paths, and performance monitoring. - Evaluate emerging agent orchestration frameworks for marketing use cases and integrate the most effective patterns. - Build and maintain LLM evaluation systems, including LLM-as-judge workflows, regression evals, guardrail testing, and quality metrics. - Diagnose performance issues across prompts, tool selection, retrieval quality, latency, cost, and task completion. - Build scalable Python services deployed in AWS, partnering with product, platform, and engineering teams to translate research into reliable platform features.

Requirements

- Bachelor's degree or higher in a quantitative field such as Statistics, Computer Science, Engineering, or Mathematics. - 5-7+ years of experience in AI/ML engineering, applied ML, NLP, or AI systems development. - 3-5 years applying AI, data science, and machine learning specifically in marketing contexts. - Strong Python engineering skills and experience building scalable production software systems. - Hands-on experience with LangGraph, LangChain, or related agent orchestration frameworks. - Experience deploying or operating systems in AWS, plus daily use of AI coding tools such as Cursor, Claude Code, or similar.

Nice to have

- Experience with Adobe Experience Platform, Adobe Commerce/Magento, Adobe Real-Time CDP, Adobe Target, or Adobe Analytics for modeling and performance analysis. - Master's degree in a quantitative field. - Experience building or using knowledge graphs, designing agent architectures with memory and tool use, or developing AI evaluation systems at scale. - Background in observability and tracing for LLM or agent systems, vector databases, model routing, or multi-model architectures. - Familiarity with enterprise automation platforms, workflow orchestration, or AI-powered business process automation.