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.

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.