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This listing was closed on Sep 19, 2026.
Browse Open JobsSenior Machine Learning (ML) Ops Engineer
Summary by AIEngineer.careers
Senior Machine Learning (ML) Ops Engineer is a MLOps Engineer role (part-time). with Docker. in REMOTE, WORLDWIDE. Compensation shown: $38K–$80K (est.). Imported listing (source: willow.2kool4u.net). Apply on the employer's site (willow.2kool4u.net).
Imported listing (source: willow.2kool4u.net) · Apply on willow.2kool4u.net
REMOTE · WORLDWIDE
$38K–$80K (est.)
Part-time
SENIOR
The sections below reproduce the third-party job description for reference. AIEngineer.careers does not write or control this text.
Imported job description
Sourced from willow.2kool4u.net
Bizmoni Corp is building an AI Super App for financial technology. They are seeking a Senior MLOps Engineer to design, automate, and optimize machine learning infrastructure, bridging data science and operations. This is a fully remote, part-time role with equity-based compensation until the next funding round.
Responsibilities
- Design, build, and maintain end-to-end machine learning infrastructure for scalability, high availability, and performance.
- Automate ML pipelines with Docker, Kubernetes, and CI/CD frameworks.
- Collaborate with data scientists and engineers to streamline model deployment.
- Monitor and optimize production models using observability tools such as Prometheus, Grafana, and ELK.
- Manage and optimize cloud infrastructure (AWS, Azure, or GCP) for efficiency and cost.
- Ensure compliance and security protocols for sensitive financial data.
- Provide support and maintenance to ensure the reliability of ML applications.
Requirements
- Proven experience as an MLOps Engineer or similar role (fintech or AI startup experience is a plus).
- Strong programming skills in Python; familiarity with Java or Scala is a plus.
Docker
San Francisco · US · 1055+ employees
Docker is a platform that simplifies the development, deployment, and management of containerized applications. It provides integrated tools for developers to build and share software, and now focuses on governance and security for AI-native development.
