ML Ops Infrastructure Engineer

About the job

Responsibilities

• Design and build CI/CD pipelines for ML model development, validation, and deployment.
• Architect and maintain model deployment pipelines from research to production.
• Build A/B testing infrastructure for controlled model rollouts and performance measurement.
• Implement comprehensive monitoring for model performance in production, including accuracy, latency, and drift.
• Develop automated retraining pipelines triggered by data changes or performance degradation.
• Create and maintain build and test environments that mirror production.
• Establish model versioning, artifact management, and rollback capabilities.
• Collaborate with research engineers to define and enforce model quality gates.
• Build observability dashboards for real-time insight into model health.
• Optimize model serving infrastructure for latency, throughput, and cost efficiency.

Requirements

• 4+ years of experience in MLOps, DevOps, or infrastructure engineering with a focus on ML systems.
• Strong proficiency in Python and experience building automation for ML workflows.
• Deep experience with CI/CD systems for software and model delivery.
• Hands-on experience with Docker and Kubernetes.
• Practical experience deploying and serving ML models in production.
• Familiarity with model evaluation, validation, and quality assurance processes.
• Understanding of monitoring and observability principles for ML systems.
• Strong problem-solving skills and a bias toward automation.