Marketing AI Engineer
New
A
Azul Systems, Inc.Marketing technology
Remote, USContractJunior
Salary35 USD per year
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Job Details
- Experience
- 1+ year of experience in AI, marketing operations, marketing technology, or a related field (fulltime roles or internships)
- Required Skills
- SalesforceLLM
Requirements
- Have at least 1 year of experience in AI, marketing operations, marketing technology, or a related field, including full-time roles or internships.
- Have hands-on experience building and scaling LLMs such as Claude, GPT, or Gemini in production or near-production environments.
- Provide a portfolio of deployed AI agents or automated workflows.
- Have experience with Model Context Protocols (MCPs) and knowledge of connecting LLMs to MarTech systems.
- Have experience building and deploying automated workflows with Zapier, n8n, or similar integration platforms.
- Be able to explain complex AI capabilities in practical terms to non-technical marketing stakeholders.
- Understand marketing principles and MarTech ecosystems, including CRM, MAP, ABM, and analytics.
- Experience with multimodal AI, including image generation, video, or audio synthesis, is a plus.
- A background in management information systems or a marketing or business major with a second degree or minor in computer science, data engineering, or software development is preferred.
Responsibilities
- Design, build, and maintain custom AI agents and agentic workflows for marketing roles.
- Connect AI capabilities to the marketing technology stack using Zapier, MCP connectors, and LLM integrations.
- Develop and maintain MCP connectors across marketing applications, ensuring reliable integrations and optimized query performance.
- Build a centralized library of prompts, skills, and brand voice frameworks, and establish AI governance standards.
- Deliver milestones from the marketing AI-first backlog, balancing strategic projects with short-term priorities.
- Lead enablement and host office hours to help the marketing team onboard to tools and troubleshoot issues.
- Share AI best practices and developments across marketing disciplines.
- Evaluate marketing AI tools and vendors against data security and integration standards.
- Explore and implement RAG customization where applicable.
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