Remote ML Ops Engineer

Description:

• We are seeking a part-time MLOps contractor to join our growing technical team.
• The role involves ensuring our models perform under load and maintaining the reliability and scalability of our ML systems.
• You will work across infrastructure and data pipelines to support model training, tuning, and deployment.
• This position is critical for someone who thrives on performance optimization, infrastructure automation, and model reliability.
• Responsibilities include monitoring and maintaining the performance of machine learning models under real-world loads and stress conditions.
• You will design, implement, and maintain scalable ML pipelines to support model experimentation, training, and deployment.
• Collaboration with data scientists and backend engineers is essential to streamline model handoff to production environments.
• You will identify and evaluate new tools and technologies to improve performance, observability, and maintainability.
• Building automated testing, CI/CD, and model validation processes to ensure model health is part of the role.
• You will also help troubleshoot production issues related to ML system behavior.

Requirements:

• Candidates must have 3+ years of experience in MLOps, DevOps, or backend roles with a focus on ML systems.
• Proficiency in Python and containerization tools such as Docker and Kubernetes is required.
• Experience with ML workflow orchestration tools like MLflow, Airflow, or Kubeflow is necessary.
• Familiarity with monitoring, logging, and alerting systems is expected.
• A solid understanding of model lifecycle management and best practices for deploying ML to production is essential.
• Candidates should be comfortable working independently in a fast-paced, early-stage startup environment.

Benefits:

• The position is part-time with an hourly or fixed weekly commitment.
• It is a remote role, with a preference for candidates in the Eastern or Central Time zones.
• The initial duration of the contract is 3–6 months, with the potential to extend or convert to full-time.
• Compensation is competitive and commensurate with experience.