Description

In this role, you will design and maintain robust machine learning infrastructure, ensuring models are efficiently deployed and monitored. You will collaborate with data scientists and engineers to enhance the reliability and scalability of ML solutions.

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 experience with SQL
• •Familiarity with monitoring and logging for ML pipelines (model drift detection, data validation, performance/feature logging)
• •Effective communication and experience partnering across engineering and data science
• •Experience with feature stores, model version management, or building internal ML platforms

Tasks

• •Design and implement scalable ML infrastructure
• •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
• •Automate observability for model performance
• •Develop internal libraries and templates for deployment
• •Implement CI/CD pipelines for model workflows
• •Mentor junior engineers and share best practices
• •Evaluate emerging MLOps technologies for improvements

Tools & Technologies

Benefits