Machine Learning Operations (MLOps) Engineer

Responsibilities:
Design and implement MLOps pipelines for model deployment and monitoring.
Collaborate with data scientists and engineers to operationalize machine learning workflows.
Optimize and automate model training, testing, and deployment processes.
Monitor model performance and ensure scalability in production environments.
Implement CI/CD pipelines for machine learning systems.

Requirements:
Proficiency in Python, Docker, Kubernetes, and cloud platforms (AWS, Azure, or GCP).
Strong understanding of machine learning lifecycle and deployment best practices.
Experience with tools like MLflow, Kubeflow, or Airflow.
Familiarity with version control systems (Git) and DevOps principles.
Bachelor’s or Master’s in AI, Machine Learning, or related field.