Description

In this role, you will design and maintain scalable machine learning infrastructure, operationalizing models in collaboration with data scientists and engineers. Your work will involve building robust pipelines, ensuring high performance, and staying updated on the latest MLOps technologies.

Requirements

• •Hands-on MLOps experience of 2+ years
• •Proficiency with infrastructure-as-code, CI/CD systems (Docker, Terraform, Jenkins, or equivalent), and at least one major cloud provider (AWS, GCP, or Azure)
• •Strong Python skills including ML libraries (scikit-learn, LightGBM, PyTorch, TensorFlow) and SQL
• •Familiarity with monitoring and logging for ML pipelines (model drift detection, data validation, performance/feature logging)
• •Effective communication skills and experience partnering across engineering and data science
• •Experience with feature stores, model version management, or building internal ML platforms

Tasks

• •Design and implement robust ML pipelines
• •Maintain reproducible pipelines for model training
• •Deploy models in batch and real-time settings
• •Build and manage ML services and APIs
• •Set up monitoring and alerting systems
• •Ensure high availability and performance
• •Develop internal libraries and templates
• •Simplify deployment workflows for model teams
• •Implement CI/CD pipelines for model workflows
• •Share best practices and mentor junior engineers
• •Evaluate emerging MLOps technologies

Tools & Technologies

Benefits