Description

You will own the end-to-end lifecycle of machine learning models, from deployment to production monitoring.

This role is on-site.

Responsibilities

• Deploy machine learning models into production environments and oversee their lifecycle.
• Implement CI/CD pipelines for model updates, rollbacks, and automated workflows.
• Design and manage scalable infrastructure for model training and deployment.
• Monitor deployed models to ensure performance tuning and compliance standards.
• Collaborate with cross-functional teams to translate business requirements into technical ML solutions.

Required Skills

• 5+ years of experience in machine learning, data engineering, or software development.
• Proficiency in Python and R programming languages.
• Hands-on experience with MLOps tools such as Kubeflow, MLflow, or Airflow.
• Experience with at least one cloud platform: AWS, GCP, or Azure.
• Strong background in building data pipelines, data cleaning, and feature engineering.
• Experience with version control systems using Git.
• Knowledge of DevOps principles, including CI/CD, Infrastructure as Code (IaC), and monitoring.
• Familiarity with container orchestration tools like Kubernetes for scaling ML workloads.

Preferred Skills

• Bachelor’s or master’s degree in computer science or a related engineering field.

Key Skills

Education

Bachelor’s or master’s degree in computer science