Azul is seeking a Marketing AI Engineer to enhance the marketing team's adoption of AI technologies. This role involves building and optimizing AI-powered tools and workflows, while collaborating closely with the marketing operations team to drive strategic initiatives.
Responsibilities:
- Deliver a steady cadence of milestones from marketing's AI-first backlog, balancing both strategic longer-term projects and tactical short-term priorities
- Help define and track role-based AI utilization goals across the marketing organization, ensuring measurable progress and accountability
- Manage the development and maintenance of MCP connectors across different marketing applications, ensuring reliable integrations and optimized query performance for each connector
- Set up centralized AI-based “skills” and governance frameworks for marketing AI usage, establishing standards and guardrails for how the team interacts with AI tools and for the Azul as a whole to adhere to marketing standards
- Lead enablement efforts and host daily office hours to support the marketing team with troubleshooting, onboarding to new tools, and answering questions
- Share best practices across the team on Skills, Prompts, and Agentic workflows, fostering a culture of continuous AI learning and improvement
- Design, build, and iterate on agentic AI workflows that automate and accelerate marketing processes across all marketing disciplines
- Explore and implement Retrieval-Augmented Generation (RAG) customization where applicable to enhance the relevance and accuracy of AI outputs — nice to have
Requirements:
- Good understanding of marketing principles and familiarity with MarTech tools and ecosystems
- Strong hands-on experience with Claude or other large language models (LLMs)
- Experience or solid working knowledge of Model Context Protocols (MCPs)
- Understanding of marketing automation and integration platforms such as Zapier
- Driven, results-oriented mindset with a track record of delivering in fast-moving environments
- Strong communicator who can translate AI capabilities into practical value for non-technical stakeholders
- Background in computer science or data engineering preferred
- Explore and implement Retrieval-Augmented Generation (RAG) customization where applicable to enhance the relevance and accuracy of AI outputs — nice to have