MLOps Engineer – Machine Learning Operations & AI Infrastructure (34639)

Design and maintain end-to-end MLOps pipelines for large-scale enterprise telecommunications AI/ML workloads. Collaborate with data scientists and engineers to deploy models to production, implement CI/CD, manage model serving infrastructure, and monitor performance and drift.

• Design and maintain end-to-end MLOps pipelines supporting model training, validation, deployment, monitoring, and automated retraining.
• Collaborate with data scientists, AI developers, and software engineering teams to transition models from research to production.
• Implement CI/CD pipelines for machine learning workflows, including automated testing and artifact management.
• Manage model versioning, experiment tracking, and governance to ensure reproducibility and auditability.
• Deploy and manage scalable model serving infrastructure using containerization and orchestration tools.
• Monitor model performance, detect drift, and implement alerting and retraining strategies.
• Optimize compute and storage infrastructure for performance, scalability, and cost efficiency.
• Document workflows, standards, and best practices related to ML lifecycle management.

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, Artificial Intelligence, or a related field.