AI Automation Engineer
Department: Marketing
Location: Makati City, Metro Manila – Hybrid
Employment Type: Full-Time
About CloudConsole
CloudConsole is a leading provider of innovative IT consulting services and IT infrastructure solutions. We are committed to helping organizations build, manage, and improve their technology environments through reliable solutions and professional technical expertise.
About The Role
CloudConsole is looking for an
AI Automation Engineer to build the technical infrastructure behind our marketing operations.
The role combines a solid software-development foundation with modern AI-assisted development. We expect the engineer to use tools such as
Claude Code, OpenAI Codex and other AI development agents extensively to build, test, debug and maintain systems quickly.
This is not a traditional developer role where every component is written manually, and it is not a pure no-code or vibe coding position.
We are looking for someone who understands how good software should be structured and operated, and who can use AI to accelerate implementation without compromising reliability, maintainability or security.
The Marketing Manager will drive business and marketing strategy. The AI Automation Engineer will translate those requirements into well-designed technical solutions.
What You Will Do
- Build and maintain marketing applications, automations, integrations and data workflows.
- Create systems for lead generation, enrichment, CRM integration, analytics, reporting, campaign operations and AI-assisted research.
- Integrate internal and external platforms using APIs, webhooks, databases and automation tools.
- Use n8n, Make, custom software, open-source tools or commercial platforms depending on what best fits the problem.
- Use Claude Code, Codex and other AI tools for development, debugging, testing, documentation and system administration.
- Deploy and maintain systems on infrastructure provided by CloudConsole.
- Ensure production systems are monitored, backed up and recoverable.
- Maintain clean source repositories and sufficient technical documentation so systems can be transferred to another engineer.
- Review AI-generated work and identify poor architecture, security issues, unreliable implementations or unnecessary complexity.
The role will initially focus on Marketing. As the capability matures, it may later support other CloudConsole departments and potentially client-facing or commercial solutions.
How We Approach Technology
We are
technology-agnostic. There is no required programming language or preferred framework. The technology should be selected based on what is appropriate for the project rather than what the developer happens to know best.
We expect AI to handle a significant amount of implementation work. The engineer's value is in understanding the problem, choosing the right architecture, directing the tools effectively and ensuring that the finished system is professionally built.
A system is not finished simply because it works.
Production Systems Should Be
- reliable and maintainable
- properly structured and version controlled
- documented
- secure
- backed up and recoverable
- reproducible and transferable
What We Are Looking For
Ideally
2–4 years of professional experience in software development, systems development, DevOps, automation engineering or a related technical role.
You Should Have Practical Experience With
- software-development fundamentals and application architecture
- APIs, HTTP, JSON and system integrations
- databases
- Git and source control
- debugging and troubleshooting
- Linux, servers or application deployment
- technical documentation
- basic security and credentials management
- modern AI-assisted development tools
- Experience with both Claude Code and OpenAI Codex is strongly preferred.
- Experience with Docker, PostgreSQL, n8n, CI/CD, CRM systems, LLM APIs, AI agents, web scraping or marketing technology is useful but not mandatory.
What Success Looks Like
You can take a business requirement and turn it into a working production system quickly.
You use AI aggressively where it saves time, but you understand the technology well enough to recognize when the output is wrong, fragile or poorly designed.
The systems you build should continue to work, be understandable by others and remain maintainable after you move on to the next project.
