MLOps Engineer
Core
Bridging Data Science and Infrastructure teams to deploy, monitor, and manage machine learning models in scalable cloud environments.
Role type
MLOps Engineer
Builds
CI/CD pipelines, containerized ML models, and cloud infrastructure on AWS
Domain
Cloud Infrastructure (AWS) + Machine Learning Operations
Deliverable
production ML models
Required skills
AWS services (SageMaker, EKS, S3, EC2, Lambda), Kubernetes, Terraform, CI/CD pipelines, Python, Bash, Linux, ML lifecycle management, monitoring/logging
Preferred skills
Serverless architectures, Helm, Data Engineering/ETL, ML frameworks (TensorFlow, PyTorch, Keras), ML platforms (Kubeflow, SageMaker), Workflow engines (Argo Workflows, Airflow)
Technologies
AWS, Kubernetes, Terraform, Docker, GitLab CI, Prometheus, Grafana, CloudWatch, ELK Stack, SageMaker, EKS, S3, EC2, Lambda
Responsibilities
Design and maintain scalable MLOps pipelines on AWS; Coordinate troubleshooting of Kubernetes clusters for model deployment; Develop and maintain CI/CD pipelines for model and application deployment; Collaborate with Data Science and DevOps teams to streamline the model development lifecycle; Implement security best practices within AWS infrastructure; Monitor and optimize data and ML pipelines for high availability; Set up and manage model monitoring systems for performance drift
Seniority
Mid-level, hands-on IC