MLOps Engineer

Description

We're looking for an MLOps / ML Engineer to design, build, and maintain the infrastructure that takes machine learning models from research to production. You'll bridge data science and software engineering, ensuring models are reliable, scalable, and easy to maintain in real-world systems.

Key Responsibilities

• Build and maintain CI/CD pipelines for training, testing, and deploying ML models
• Containerize and deploy models (Docker, Kubernetes) to production environments
• Develop and manage model serving infrastructure (REST/gRPC APIs, batch and real-time inference)
• Monitor deployed models for performance drift, latency, and data quality issues
• Automate retraining pipelines and version control for datasets, features, and models
• Collaborate with data scientists to productionize experimental models
• Implement logging, alerting, and rollback strategies for model reliability
• Optimize infrastructure cost and performance across GCP platforms