Senior MLOps Engineer

Overview

Senior MLOps Engineer to design, build, and run end-to-end ML infrastructure and pipelines—covering training support, deployment, and continuous monitoring.

What you'll do

• Design, build, and maintain end-to-end ML infrastructure and pipelines.
• Develop and manage CI/CD pipelines for automated ML model delivery.
• Implement model registry, experiment tracking, and versioning using tools such as MLflow or SageMaker Model Registry.
• Establish monitoring, observability, and alerting for drift, degradation, and anomalies.
• Collaborate with data scientists to productionize ML models and manage the full model lifecycle.
• Optimize ML workflows for performance, scalability, and cost across training and inference.
• Integrate ML services into distributed systems with data and software engineering teams.

What you'll need

• 8+ years of experience in MLOps, ML infrastructure, or DevOps for AI/ML systems.
• Experience using MLflow and/or SageMaker for experiment tracking and model lifecycle management.
• Experience with Airflow or Step Functions for workflow orchestration.
• Deep knowledge of CI/CD and automation frameworks (e.g., GitHub Actions, Terraform, CloudFormation).
• Hands-on experience with Docker containerization and EKS orchestration.
• Proficiency in Python and scripting for ML integrations.
• Strong knowledge of AWS and relevant ML services (SageMaker, Lambda, S3, Kinesis, Step Functions).
• Understanding of security, compliance, and governance in ML production systems.

Details

• Hybrid work schedule is referenced via job modality selection.