Machine Learning Operations (MLOps) Engineer

About the Role

We are looking for a Machine Learning Operations (MLOps) Engineer specializing in data labeling workflows to support and optimize our annotation pipeline. This role will be responsible for troubleshooting platform issues, automating annotation processes, and managing API integrations to ensure high-quality labeled data for machine learning models. The ideal candidate has strong Python development skills, experience with data annotation tools (e.g., Labelbox, Scale AI, CVAT), and a passion for optimizing ML workflows.

Key Responsibilities

Platform Support & Troubleshooting

• Act as the technical lead for data annotation tools such as Labelbox, Scale AI, CVAT, and others.
• Monitor support channels, troubleshoot platform issues, and ensure smooth annotation workflows.
• Work closely with ML engineers, data scientists, and annotation teams to resolve integration challenges.
• Manage onboarding & access for internal teams using the annotation platform’s QA environment.

Automation & Development

• Develop and maintain Python scripts to optimize data labeling pipelines and reduce manual work.
• Build automation tools for data ingestion, annotation, and transformation.
• Ensure seamless SDK & API integrations between the annotation platform and internal ML workflows.
• Implement updates to SDK versions and support new platform feature releases.

Process Optimization & Documentation

• Identify and execute opportunities to streamline data launch and download processes.
• Improve annotation workflow efficiency, ensuring faster turnaround times.
• Create and maintain technical documentation for troubleshooting, workflows, and best practices.

Required Qualifications

3+ years of Python development experience, with a focus on SDKs, REST APIs, and automation.

Experience working with data labeling tools (e.g., Labelbox, Scale AI, CVAT, Amazon SageMaker Ground Truth).

Strong background in data pipeline development, ingestion, and dataset management.

Ability to debug and troubleshoot automation failures in data annotation workflows.

Experience working in MLOps, data engineering, or machine learning data workflows.

Strong communication skills and ability to work in a cross-functional environment.

Preferred Qualifications

Experience with Apache Airflow for data pipeline automation.

Familiarity with JavaScript and frontend development tools for UI optimizations.

Background in quality control and annotation process improvements.

Experience integrating multiple data annotation platforms into ML pipelines.