Description

You will operationalize machine learning models by building CI/CD pipelines, managing production infrastructure, and ensuring model reliability in live environments.

This role is hybrid.

Responsibilities

• Build and maintain CI/CD pipelines for ML workflows in collaboration with data scientists.
• Design and manage scalable deployment architectures using Docker, Kubernetes, and infrastructure-as-code tools.
• Monitor model performance and data drift using Prometheus, Grafana, or custom solutions.
• Automate data pipelines and retraining workflows using orchestration tools like Airflow or Kubeflow.
• Provision infrastructure using Terraform and AWS CloudFormation, and lead technical workshops with clients during PST hours.

Required Skills

• 4–5 years of hands-on experience in MLOps or production ML environments.
• Proficiency in Python and Bash/Shell scripting.
• Experience with cloud platforms including AWS, Azure, or GCP.
• Hands-on expertise with Terraform and CloudFormation.
• Proficiency in Docker and Kubernetes.
• Experience with CI/CD tools such as Jenkins, GitHub Actions, or GitLab CI/CD.
• Knowledge of ML lifecycle tools including MLflow, DVC, Kubeflow, or SageMaker.
• Experience with workflow orchestration via Apache Airflow or Prefect.
• Competency in monitoring and logging with Prometheus, Grafana, or ELK stack.

Preferred Skills

• Bachelor’s or Master’s degree in Computer Science, Data Science, or Engineering.
• Experience with large-scale data systems, distributed computing, and model governance.

Key Skills

Education

Bachelor’s or Master’s degree in Computer Science