AI Engineer - 18586

Role: AI Engineer (Machine Learning Engineer)
Priority Location: LATAM
Working Hours: PST (Pacific Standard Time)

This is a full-time remote role working with a U.S.-based team. Candidates must be comfortable with some overlap with U.S. working hours.

Type of contract: Independent Contractor
Type of job: Remote

The final offer is at the client’s discretion and will depend on the candidate’s interview result, skills, and experience.

A data-driven marketing technology organization specializing in advanced analytics and optimization solutions. By leveraging machine learning and robust data infrastructure, it drives measurable business impact at scale.

As an AI Engineer (Machine Learning Engineer), you will own and operate machine learning systems in production. This role focuses on production environments, orchestration, and infrastructure rather than pure experimentation.

You will act as a key connector between Data Science, Data Engineering, and AI systems, ensuring models are successfully deployed, integrated, and maintained in real-world applications. This is a hands-on role requiring strong ownership of ML pipelines, system performance, and reliability.

• →Own and maintain ML pipelines in production environments, including batch and orchestration-heavy workflows
• →Build and manage end-to-end data and ML pipelines for both training and inference
• →Orchestrate workflows using tools such as Flyte or similar platforms
• →Collaborate closely with Data Scientists to productionize machine learning models
• →Integrate ML and AI components across various systems and services
• →Ensure system reliability through monitoring, performance optimization, and troubleshooting
• →Partner with Data Engineers on data infrastructure and pipeline development
• →Apply best practices in MLOps, documentation, and scalable system design

• 3–5+ years of experience in Machine Learning Engineering or similar roles
• Strong proficiency in Python and SQL
• Experience building and maintaining ML pipelines end-to-end
• Solid understanding of ML systems, deployment processes, and orchestration
• Hands-on experience with MLOps practices
• Strong knowledge of infrastructure and system design

• Experience with Flyte or similar orchestration tools
• Exposure to LLMs or AI systems
• Experience working with marketing data, attribution models, or forecasting
• Familiarity with Kubernetes or cloud environments

• Strong ownership mindset with the ability to manage projects end-to-end
• Cross-functional collaboration skills across Data Science, Engineering, and AI teams
• Problem-solving mindset with a focus on system reliability and efficiency
• Detail-oriented with a structured and process-driven approach

• Full-time, fully remote position
• 11 US holidays + 3 weeks of paid time off
• Performance-based bonus program