Description:

We are seeking an experienced ML Ops Engineer to build and optimize the infrastructure powering machine learning operations within a dynamic and collaborative environment.
This role involves designing robust data pipelines, automating CI/CD workflows for ML models, and implementing scalable, reproducible systems that enable seamless deployment and monitoring.
Responsibilities include building and managing automated, reproducible ML pipelines for data ingestion, training, validation, deployment, and monitoring.
The engineer will develop scalable model-serving architectures, including containerized deployments, APIs, and real-time frameworks.
Automating infrastructure workflows and establishing CI/CD pipelines for ML models with robust versioning and rollback mechanisms is essential.
Monitoring model performance and data drift in production to ensure accuracy and system health is a key responsibility.
Collaboration with cross-functional teams to promote best practices for ML reproducibility, scalability, and maintenance is required.

Requirements:

Candidates must have 5+ years of experience in ML Ops, Data Engineering, or DevOps roles.
Proficiency with cloud platforms such as GCP, AWS, or Azure, and ML Ops tools like Kubeflow, MLflow, or SageMaker is necessary.
Expertise in containerization (Docker, Kubernetes) and CI/CD pipelines (GitLab CI, Jenkins, CircleCI) is required.
Hands-on experience with data pipeline orchestration tools like Airflow is essential.
Strong knowledge of data versioning, feature stores, and model lifecycle management is expected.

Enjoy 100% remote work, allowing you to thrive from your preferred location with just a laptop and a reliable internet connection.
Receive highly competitive USD pay, offering excellent, market-leading compensation that exceeds typical market offerings.
Benefit from paid time off policies that ensure you have the opportunity to unwind and recharge when needed.
Experience work with autonomy, managing your time freely as long as the work gets done, focusing on results rather than hours.
Grow your expertise by working on innovative, high-impact projects with industry-leading U.S. companies.