MLOps Engineer

MLOps Engineer

XM is seeking an experienced MLOps Engineer to bridge the gap between Data Science and Infrastructure teams. You will be responsible for designing, implementing, and maintaining scalable machine learning operations pipelines while supporting both data science and DevOps initiatives within a hybrid work environment.

Key Responsibilities

• Design and implement scalable MLOps pipelines on AWS using SageMaker, EC2, EKS, S3, Lambda and related services

• Develop and maintain CI/CD pipelines for model and application deployment with comprehensive testing and monitoring

• Coordinate with platform teams to manage and troubleshoot Kubernetes (EKS) clusters for microservice deployment

• Collaborate with Data Science and DevOps teams to streamline the model development lifecycle from experimentation to production

• Implement security best practices including network security, data encryption, and role-based access controls

• Monitor and optimize data and ML pipelines for high availability and performance

• Establish model monitoring systems to detect performance drift and enable continuous improvement

Requirements

• Bachelor's degree in Computer Science, Engineering, or related field

• 2+ years of hands-on experience in MLOps, DevOps, or related fields

• Proficiency in Python and Bash with Linux environment expertise

• Strong knowledge of AWS ML services (SageMaker, EKS, S3, EC2, Lambda)

• Hands-on experience with Kubernetes, Docker, and infrastructure-as-code tools (Terraform or CloudFormation)

• Familiarity with CI/CD tools such as GitLab CI

• Understanding of ML model lifecycle and monitoring solutions (Prometheus, Grafana, CloudWatch, ELK Stack)

• Knowledge of networking concepts and cloud security best practices

Advantageous experience includes serverless architectures, Helm, Data Engineering pipelines, ML frameworks (TensorFlow, PyTorch, Keras), and workflow engines (Argo Workflows, Airflow).