New opportunity

Marketing AI Engineer

Elastic · Bangalore, India

About this role

The Marketing AI Engineer will play a critical role in the Marketing AI, Operations and Automation team at Elastic. The primary purpose of this role is to design and build automation infrastructure that powers marketing at scale, connecting systems via APIs and webhooks and integrating AI models into live workflows. Responsibilities include designing automated marketing workflows using no-code/low-code platforms, integrating marketing platforms and third-party services, configuring system-level prompts and agent behavior, and calling LLM APIs to trigger AI reasoning. The role requires gathering requirements from cross-functional marketing teams, ensuring workflow reliability and maintainability, and documenting complex automation logic. Required qualifications include 4+ years of experience in marketing operations or marketing technology engineering, hands-on experience with no-code/low-code platforms (e.g., Workfront Fusion, Zapier, Make, n8n), practical experience with APIs and webhooks, and experience working with AI models or generative AI platforms in a workflow context. Preferred qualifications include experience with Workfront, Marketo, Adobe Fusion, and Google Suite, as well as proficiency in Google Apps Script, JavaScript, or Python. The position is part of a globally distributed team and requires strong documentation habits and the ability to translate non-technical requirements into technical specifications. Elastic offers competitive pay, health coverage, flexible schedules, and generous parental leave.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Marketing OperationsMarketing TechnologyAutomationNo-code platformsLow-code platformsWorkfront FusionZapierMaken8nAPIsWebhooksLLMsGenerative AIPrompt EngineeringWorkfrontMarketoGoogle WorkspaceGoogle Apps ScriptJavaScriptPython

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Building Scalable Marketing Automation Infrastructure
The core mandate is to design and implement robust, automated workflows using no-code/low-code platforms like Workfront Fusion or Zapier. Success requires ensuring data flows reliably between marketing systems without manual intervention, necessitating a focus on system architecture and integration stability.
Integrating AI Models into Marketing Workflows
You must programmatically call LLM APIs to trigger AI reasoning within existing processes. This involves configuring system-level prompts, managing agent behavior, and ensuring that AI-generated outputs are structured, repeatable, and correctly routed back into the marketing tech stack.
Cross-Functional Requirement Translation
You will act as a bridge between non-technical marketing teams and technical execution. You must gather business requirements from various departments and translate them into buildable, maintainable technical specifications that prioritize reuse and cross-team impact.
Documentation and System Maintenance
Given the complexity of automated logic, you are responsible for the long-term reliability of your builds. This requires maintaining clear, comprehensive documentation that allows non-technical stakeholders to understand the logic, dependencies, and operational flow of the automation infrastructure.

Likely challenges to think through

Managing API Complexity and Error Handling
Integrating disparate marketing platforms via APIs and webhooks introduces risks regarding authentication, data formatting, and system failures. Prepare by demonstrating your methodology for building resilient error-handling routines and managing API rate limits or authentication changes in a production environment.
Balancing No-Code Flexibility with Technical Debt
While no-code tools enable speed, they can become difficult to maintain as logic grows complex. You may face the challenge of deciding when to transition from a no-code solution to custom scripting (JavaScript/Python). Prepare by discussing your criteria for evaluating tool scalability versus custom development.
Ensuring AI Output Consistency at Scale
Generative AI can be non-deterministic, which poses a risk to marketing workflows requiring high precision. Prepare by explaining your approach to prompt engineering, output validation, and creating guardrails that ensure AI-driven tasks produce consistent, reliable results across different use cases.

Practice your answers

Technical practice

Designing Resilient Marketing Workflows

What to coverDescribe your approach to building multi-step workflows in platforms like Make or Zapier. Focus on how you handle error states, data validation, and ensuring reliability when connecting disparate marketing systems.

API Integration and Authentication

What to coverExplain your process for integrating third-party services via APIs. Discuss how you manage different authentication methods, handle rate limiting, and ensure secure data transmission between systems.

LLM API Implementation

What to coverDetail your experience calling LLM APIs within an automated workflow. Discuss how you construct prompts programmatically, manage model responses, and ensure the output is structured correctly for downstream systems.

System-Level Prompt Engineering

What to coverHow do you configure system-level prompts and agent behavior to ensure consistent outputs at scale? Explain the trade-offs between rigid prompt structures and more flexible, context-aware configurations.

Handling No-Code Limitations

What to coverWhen a no-code platform reaches its functional limit, how do you decide between finding a workaround or using custom code like JavaScript or Python? Provide an example of when you have successfully extended a workflow with custom scripting.

Data Mapping and Transformation

What to coverDescribe your strategy for mapping data between marketing platforms like Marketo and other tools. How do you ensure data integrity and consistent formatting across complex, multi-step integrations?

Documentation for Non-Technical Stakeholders

What to coverHow do you document complex automation logic so that non-technical marketing team members can understand the workflow's purpose and function? Suggestion: Prepare to discuss your documentation habits and how you balance technical detail with accessibility.

Evaluating New Integration Tools

What to coverWhat criteria do you use to evaluate new tools or integration patterns for a marketing tech stack? Explain how you assess technical fit, scalability, and the potential for cross-team impact.

Behavioural practice

Translating Requirements into Technical Specs

What to coverDescribe a time you had to gather requirements from a non-technical stakeholder and translate them into a buildable technical specification. Focus on how you clarified ambiguous needs and ensured the final output met their expectations.

Managing Cross-Functional Collaboration

What to coverGive an example of working with multiple marketing teams (e.g., demand generation, content, operations) to identify and remove friction. How did you balance competing priorities to deliver a solution that provided cross-team value?

Working in a Distributed Environment

What to coverShare your experience working within a globally distributed team across different time zones. How do you maintain communication, ensure project momentum, and manage dependencies effectively?

Adapting to Changing Tech Stacks

What to coverDescribe a situation where you had to adapt to a significant change in your marketing technology stack. How did you approach learning the new tools and ensuring existing workflows remained reliable during the transition?

Sharpen your knowledge

Topics to prepare

Marketing Automation Architecture
Focus on designing scalable, multi-step workflows that integrate disparate marketing platforms. Exercise: Map out a hypothetical lead-nurturing workflow that triggers an LLM for content personalization and routes the output to a CRM, ensuring you account for error handling and data validation at each step.
LLM Integration and Prompt Engineering
Understand how to programmatically call LLM APIs and configure system-level prompts for consistent, structured outputs. Exercise: Create a prompt template designed to extract specific metadata from unstructured marketing copy and test how different system instructions affect the reliability of the JSON output.

Skills to revise

No-Code/Low-Code Workflow Development
Demonstrate proficiency in platforms like Zapier, Make, or n8n. Exercise: Build a functional automation that watches for a new entry in a Google Sheet, sends that data to an AI API, and updates a secondary system with the processed result.
API and Webhook Integration
Master the ability to connect systems via REST APIs and webhooks. Exercise: Practice constructing authenticated HTTP requests (using tools like Postman or cURL) to interact with a public API, focusing on handling authentication tokens and parsing JSON responses.
Technical Documentation for Stakeholders
Develop the ability to translate complex automation logic into clear, non-technical documentation. Exercise: Write a one-page 'runbook' for a complex workflow you have built, explaining the trigger, the logic, the AI model's role, and how a non-technical user should troubleshoot common failures.

Your preparation checklist

Prepare a portfolio of automated marketing workflows
Document 2-3 examples of multi-step workflows you have built using platforms like Zapier, Make, or n8n. Focus on how you connected disparate marketing systems, handled API authentication, and managed error states to ensure data reliability.
Develop an AI integration case study
Create a brief technical walkthrough of a project where you integrated an LLM API into a business process. Highlight how you constructed system-level prompts, handled structured JSON outputs, and routed AI-generated insights back into a CRM or marketing platform.
Draft a sample technical specification document
Prepare a template or example of how you translate non-technical marketing requirements into a technical build document. This should demonstrate your ability to explain complex automation logic and system dependencies to non-technical stakeholders.
Review your experience with the Elastic marketing stack
Prepare to discuss your hands-on experience with tools like Marketo, Workfront, or Google Workspace. Be ready to explain how you have used these tools in conjunction with APIs or low-code platforms to solve specific marketing operations challenges.
Formulate questions about Elastic's automation infrastructure
Prepare thoughtful questions regarding how Elastic currently manages its marketing tech stack, the primary bottlenecks the team faces, and how they balance the use of no-code platforms versus custom scripts for complex integrations.

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