Lead / Principal MLOps Engineer

Job Summary

We are seeking a highly skilled Lead / Principal MLOps Engineer to design, build, and operate secure, scalable, and reliable enterprise ML platform infrastructure. This role will lead the end-to-end operationalization of machine learning workloads-from training and experimentation through deployment, serving, monitoring, and lifecycle management.

Responsibilities

• Design, build, and maintain scalable, secure, highly available, and reliable MLOps and ML platform infrastructure.
• Lead end-to-end ML pipelines covering training, validation, deployment, serving, monitoring, and lifecycle management.
• Build and manage Kubernetes-based model deployment and serving infrastructure.
• Implement robust CI/CD pipelines for ML applications, models, and platform infrastructure.
• Manage Infrastructure as Code and automate cloud provisioning, configuration, and environment management.
• Design and optimize Kubernetes environments for high availability, scalability, disaster recovery, security, and efficient resource utilization.
• Implement monitoring and observability solutions across ML models, pipelines, applications, infrastructure, and platform services.
• Monitor and manage model performance, data drift, concept drift, reliability, and operational health.
• Optimize cloud and GPU infrastructure for performance, scalability, reliability, and cost efficiency.
• Lead troubleshooting, incident management, root cause analysis, and production issue resolution. Collaborate with Data Science, ML Engineering, Platform Engineering, and Cloud teams to improve ML workflows.
• Establish MLOps best practices, engineering standards, architecture patterns, governance controls, and documentation.
• Provide technical leadership, mentoring, architecture guidance, and infrastructure/code reviews..

Qualifications

• 8+ years of overall engineering experience, including at least 4 years of hands-on experience in MLOps, ML Platform Engineering, or Cloud Engineering.
• Strong hands-on experience designing and operating production MLOps or ML platform environments.
• Strong understanding of ML lifecycle management: experimentation, model versioning, model registry, training, deployment, serving, monitoring, and governance.
• Hands-on experience with one or more: MLflow, Kubeflow, Apache Airflow, Argo Workflows, Weights & Biases (W&B), or Amazon SageMaker.
• Advanced experience with Docker and Kubernetes, including EKS, GKE, or AKS.
• Strong practical experience with Helm and KServe for Kubernetes-based application and model deployment/ serving.
• Strong experience with GitHub Actions, GitLab CI, or Jenkins to automate build, test, deployment, and release processes.
• Strong cloud-platform experience in AWS, Azure, or GCP; multi-cloud or hybrid-cloud experience is preferred.
• Strong experience with ML pipeline orchestration, model deployment and serving, model monitoring, data drift and concept drift detection, autoscaling, HA, DR, incident management, and RCA.
• Experience optimizing cloud and GPU infrastructure for performance, scalability, reliability, and cost efficiency.
• Strong problem-solving, technical leadership, communication, stakeholder-management, and mentoring abilities.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.