You are:
An experienced MLOps Engineer who thrives on building the backbone of production-grade ML systems. You enjoy bridging the gap between model development and production, creating scalable infrastructure, and empowering ML teams to ship models with confidence. You’re comfortable working across cloud services, containerized environments, and CI/CD pipelines—and you understand the importance of reproducibility, monitoring, and automation.
You will:
Design and implement infrastructure for ML model training, testing, deployment, and monitoring.
Collaborate with ML engineers and data scientists to streamline model operationalization and CI/CD integration.
Manage containerized environments (Docker, Kubernetes/ECS) and model serving infrastructure.
Monitor production ML systems for performance degradation, data drift, and retraining needs.
Use tools like MLflow to track experiments, manage model versions, and support model governance.
Automate workflows using tools like Airflow, Step Functions, or similar orchestration platforms.
Contribute to IaC (e.g., Terraform or CloudFormation) to ensure reproducible infrastructure deployments.
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