Specifically, you will: Designing CRM‑ready AI agents that operate across data ingestion, signal normalization, decisioning logic, and activation triggers Translating CRM strategies into operational agent architectures that account for: Data latency, quality, and ownership Identity resolution and consent constraints Channel orchestration rules and suppression logic Integrating agent workflows into existing CRM stacks, including CDPs, journey orchestration tools, marketing clouds, and analytics platforms Partnering with platform and solution partners (e.g., CDP, identity, activation, analytics) to define where the agent layer sits-and how it interoperates Building or overseeing working agent prototypes that can plug into partner environments for pilots, POVs, or live programs Establishing operational guardrails: human‑in‑the‑loop checkpoints, QA frameworks, escalation paths, and monitoring Creating reusable agent patterns and operating models that teams can deploy consistently across clients and partners Supporting new business by demonstrating how AI agents work with, not against, enterprise CRM ecosystems Qualifications Who Thrives in This Role This role is ideal for someone who: Has deep experience in CRM, lifecycle marketing, or marketing technology Understands segmentation logic, journey triggers, suppression rules, and channel orchestration Has spent the last 1-2 years actively building with LLMs, agents, or AI orchestration tools Is energized by ambiguity and moves quickly from concept to output Thinks in inputs, decisions, and outcomes, not features or channels Is comfortable being both strategic and hands‑on, often in the same day You don't need to come from a traditional agency background, but you do need to be able to operate in fast‑paced, client‑facing environments. What You Bring 10+ years of experience in CRM strategy, marketing technology, data‑driven programs, or adjacent fields Hands‑on experience with LLM‑based tools, agent frameworks, or AI orchestration platforms Strong understanding of CRM data models and lifecycle logic Ability to write clear, practical functional specifications for technical and non‑technical partners Enough technical fluency to collaborate credibly with engineers (Python, JavaScript, APIs, or equivalent) A track record of shipping real artifacts, not just concepts You should be able to point to something tangible you've built with AI: a prototype, an internal tool, a deployed agent, or a working workflow.
