MLOps Engineer
Core
Bridging Data Science and Infrastructure teams to support the ML lifecycle from experimentation to production deployment.
Role type
MLOps Engineer
Builds
Scalable MLOps pipelines, CI/CD pipelines, and cloud infrastructure for ML models and microservices.
Domain
Cloud Infrastructure (AWS) and Machine Learning Operations
Deliverable
production ML models
Required skills
AWS services (SageMaker, EKS, S3, EC2, Lambda), Kubernetes, Docker, Terraform, GitLab CI, Python, Bash, Linux, ML model lifecycle management, monitoring and logging (Prometheus, Grafana, CloudWatch, ELK)
Preferred skills
Serverless architectures, Helm, Data Engineering pipelines, ML frameworks (TensorFlow, PyTorch, Keras), ML platforms (Kubeflow, SageMaker), workflow engines (Argo Workflows, Airflow)
Responsibilities
Design and maintain scalable MLOps pipelines on AWS; coordinate with platform team to troubleshoot Kubernetes clusters; develop and maintain CI/CD pipelines; collaborate with Data Science and DevOps teams to streamline model development; implement security best practices; monitor and optimize data and ML pipelines; set up model monitoring systems for performance drift.
Seniority
Mid-level, hands-on IC
