Description

You will design and implement cloud-based MLOps architectures and orchestration pipelines for production ML systems.

This role is on-site.

Responsibilities

• Design and implement MLOps architectures on AWS, Azure, or GCP.
• Build CI/CD/CL and Continuous Training (CT) pipeline orchestrations.
• Deploy machine learning models into production environments.
• Monitor and maintain model performance and infrastructure health.
• Collaborate with data scientists and engineers to integrate models into production.

Required Skills

• 12+ years of total professional experience.
• 3+ years specifically as an MLOps Architect.
• 5+ years of experience in an MLOps Engineer role.
• Proven track record of independently handling at least three production deployments.
• Experience with Kubeflow, MLflow, and Kubernetes.
• Proficiency with GitLab CI, GitHub Actions, or CircleCI.
• Experience with Airflow or similar orchestration tools.
• Knowledge of Agile practices.
• Background in Computer Vision and Predictive Analytics.

Preferred Skills

• Experience with multi-cloud MLOps environments.
• Advanced knowledge of model monitoring and drift detection.

Key Skills

Education

Any Graduate