MLOps Engineer (26-09599) -

MLOps Engineer

About the Role

The MLOps Engineer builds and operates the infrastructure, pipelines, and tooling that carry AI/ML and GenAI models from development into reliable production systems.

Reporting into the Chapter Lead

AI Engineering, the role owns deployment automation, monitoring, and the CI/CD practices that keep models running safely at scale — bridging AI Engineering, Data Engineering, and platform/DevOps disciplines.

Key Responsibilities

• Build and maintain CI/CD pipelines for deploying ML and GenAI models into production environments.
• Implement model monitoring, logging, and alerting to detect performance degradation, drift, or unexpected behavior.
• Manage model versioning, experiment tracking, and model registry practices (e.g., MLflow, SageMaker, or equivalent).
• Containerize and orchestrate model-serving infrastructure (Docker, Kubernetes) for scalable, low-latency inference.
• Automate retraining pipelines and rollback mechanisms to safely recover from model or deployment issues.
• Collaborate with AI Engineers and Data Engineers to productionize models and GenAI applications reliably.
• Ensure model deployments adhere to data security, governance, and compliance requirements.

Key Skills & Experience

• 3–6 years in MLOps, DevOps, or ML engineering roles, including hands-on production deployment experience.
• Proficiency with MLOps/LLMOps platforms such as MLflow, Kubeflow, or SageMaker.
• Hands-on experience with Docker, Kubernetes, and CI/CD tooling for automated, reproducible deployments.
• Working knowledge of at least one ML framework (TensorFlow, PyTorch, or Scikit-learn) and one cloud platform (Azure, AWS, or GCP).
• Experience with monitoring/observability tooling for ML systems (drift detection, performance dashboards).
• Familiarity with LLM-specific serving challenges (GPU resource management, inference optimization) is a plus given the growing GenAI/LLMOps scope of this function.
• Relevant certification (AWS Certified Machine Learning – Specialty, Google Professional ML Engineer, Azure AI Engineer Associate, or CKA) is a plus, not required.
• Bachelor's degree in Computer Science, Engineering, or related technical field.