Description

You will manage the deployment, monitoring, and maintenance of machine learning models in production environments.

Responsibilities

• Operationalize ML models by building CI/CD pipelines for ML workflows in collaboration with data scientists.
• Design and maintain scalable deployment architectures using Docker, Kubernetes, and infrastructure-as-code.
• 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 and manage infrastructure using Terraform and AWS CloudFormation.
• Lead technical workshops, build mockups, and coordinate directly with clients during PST business hours.

Required Skills

• 4–5 years of hands-on experience in MLOps or production ML environments.
• Mandatory 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.
• Proven ability to communicate technical content to clients and cross-functional teams.

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