Requirements

Operating system

Windows

Our requirements

• 5+ years of experience in Machine Learning Engineering or MLOps roles
• Solid Python development skills
• Strong hands-on experience with Airflow (MWAA), MLFlow, and/or SageMaker
• Familiarity with ML observability tools such as Grafana, custom metric logging, model drift detection, and alerting mechanisms
• Proficiency in building CI/CD pipelines for ML systems with automated testing and validation
• Experience with Infrastructure-as-Code tools (CloudFormation, YAML)
• Understanding of secure and compliant deployment of ML pipelines
• Excellent debugging and problem-solving skills
• Experience with OpenAI API usage in production, containerization, and Kubernetes orchestration is highly valued

Your responsibilities

• Build and automate end-to-end ML workflows using Airflow (MWAA), integrating with feature stores, training, fine-tuning, deployment, and monitoring systems
• Implement ML model lifecycle management using MLFlow and/or SageMaker (preferred)
• Design and deploy infrastructure for ML workflows using Infrastructure-as-Code tools such as CloudFormation and YAML
• Monitor and maintain ML services, leveraging observability tools (Grafana, custom metric logging, drift detection, and alerting)
• Ensure robust testing of ML pipelines, including data validation, integration/unit tests, and performance monitoring within CI/CD workflows
• Champion MLOps best practices and help enforce security and compliance standards (e.g., secrets management, training data retention)
• Contribute to central feature store development and component standardization efforts
• Integrate and optimize OpenAI APIs in production environments, with focus on prompt engineering and token handling
• Deploy containerized ML applications using Docker and Kubernetes