remote
Machine Learning Operations Engineer - AI Trainer - 10xteam
ML Engineer
Freelance ML Ops Engineer tasked with training and deploying AI models in production. Leverage Python, Docker, Kubernetes, and AWS to build scalable pipelines and ensure model reliability on a flexible 8‑20 hour schedule.
About the role
Key Responsibilities
- Design, implement, and maintain end‑to‑end ML pipelines for training, validation, and deployment of AI models.
- Containerize models using Docker and orchestrate with Kubernetes for scalable, reproducible deployments.
- Integrate models into production environments on AWS, managing CI/CD, monitoring, and automated rollback.
- Collaborate with data scientists to refine training datasets, feature engineering, and hyperparameter tuning.
- Document processes, create runbooks, and provide training to internal teams on MLOps best practices.
Requirements
- Proven experience in Python and ML frameworks (TensorFlow, PyTorch, Scikit‑learn).
- Hands‑on expertise with Docker, Kubernetes, and cloud services (AWS SageMaker, ECS, EKS).
- Strong understanding of CI/CD pipelines, model versioning, and monitoring tools.
- Excellent problem‑solving skills and ability to work independently in a remote, flexible schedule.
Skills
pythonmachine learningmlopsdockerkubernetesaws