Senior MLOps Engineer

Role Introduction

Operationalizes ML models within the stack using GitLab CI/CD, Docker, Kubernetes, and Airflow for retraining pipelines, alongside production model monitoring and drift detection.

Features

• Onsite

Requirements

• Build and maintain CI/CD pipelines specifically for ML model deployment, versioning, and rollback (GitLab CI).
• Containerize ML models and manage deployment via Kubernetes, ensuring scalable and reliable serving infrastructure.
• Design and operate Airflow-based retraining pipelines, scheduling and monitoring model refresh cycles.
• Implement production model monitoring, performance metrics, latency, and resource usage.
• Build drift detection mechanisms to flag when models need retraining or investigation.
• Maintain model registries and versioning to ensure traceability across model lifecycle stages.
• Partner with the AI/ML Engineer and AI S...