AI Automation Engineer

About Us:

At Near, we help top talent in Latin America find remote roles with US companies.

About the Role:

We're looking for an AI Automation Engineer (Data & Creative Systems) who combines solid software engineering fundamentals with a strong focus on cloud data infrastructure, reinforcement learning, and creative content automation. You'll design, build, and maintain custom-coded automations and internal tools, and collaborate with other engineers and stakeholders to deliver robust, scalable solutions.

This role requires someone proactive and solution-oriented, who is comfortable dealing with ambiguity, takes ownership of problems end-to-end, and delivers high-quality results in a fast-moving environment.

You Will:

• Design, implement, and maintain custom-coded automations and backend services for both Near and our clients.
• Build and manage data infrastructure on GCP, including data warehouses, pipelines, and integrations between systems.
• A primary focus will be building a 'Brand Brain' - a context layer that ingests product reviews, brand guidelines, and competitor ad libraries to ensure AI-generated content is indistinguishable from human-made creative
• Develop reinforcement learning systems that improve based on feedback, applying them to automation and content workflows.
• Build automations for creative content generation targeting platforms like Meta and TikTok, integrating AI tools to produce relevant, scalable outputs.
• Integrate AI tools into existing infrastructure, providing business context to the AI for more targeted and relevant outputs.
• Design, structure, and maintain relational databases; implement data migrations and integrations between systems.
• Write reliable, well-tested code and set up safe testing practices for new and existing features.
• Contribute to system and architecture design for automation and data-related projects.
• Collaborate closely with other automation engineers, stakeholders, and clients to clarify requirements and deliver solutions aligned with business needs.
• Continuously improve existing automations, identifying bottlenecks and proposing technical enhancements.
• Document architecture, workflows, and implementation details to enable knowledge sharing within the team.

About You:

Your Background:

• Strong experience as a data engineer, software engineer, backend engineer, or AI/ML engineer with strong coding responsibilities.
• Solid experience with GCP, particularly for building and managing data infrastructure (e.g., BigQuery, Dataflow, Cloud Storage).
• Hands-on experience with reinforcement learning (RL) - designing feedback loops and deploying RL-based systems.
• Experience building automations for creative workflows, especially for generating content for platforms like Meta or TikTok.
• Expertise in AI-native development environments (e.g., Claude Code) and workflow orchestration platforms (e.g., n8n) to accelerate the build-to-deploy cycle
• Experience integrating AI tools into existing infrastructure and providing business context to the AI for more relevant outputs.
• Strong Python skills in data or backend contexts (e.g., data processing, ML pipelines, analytics).
• Experience working with relational databases (SQL), including schema design, queries, and data migrations.
• Strong understanding of software engineering fundamentals: system design, architecture, testing, and code quality.
• Comfortable working in Agile environments (Scrum or similar), collaborating in a distributed team.
• Excellent communication skills - comfortable working in a remote, English-speaking environment.
• Self-motivated, proactive, and accountable - you take ownership of outcomes and follow through.
• Able to work independently, manage ambiguity, and adapt to changing priorities without losing execution quality.

Nice to Have:

• Strong experience working with Python to build and manage data or AI systems.
• Experience with other cloud platforms (AWS, Azure) in addition to GCP.
• Background with data-focused frameworks, data analytics, or data-intensive applications (e.g., dbt, Airflow, Spark).
• Familiarity with infrastructure concepts and system design beyond pure coding.
• Experience in automation-heavy environments (internal tooling, process automation, workflow orchestration).