Machine Learning Operations Specialist | AI Platform Engineer

Role overview

Platform and MLOps responsibilities

• Design and maintain scalable ML infrastructure and platforms for development, training, deployment, monitoring, and lifecycle management.
• Automate model training, validation, deployment, retraining, and CI/CD; manage model versions, artifacts, experiment tracking, and reproducibility.
• Deploy models across cloud, on-premises, or hybrid environments and build scalable model-serving for high-volume inference.
• Monitor performance, drift, prediction quality, system health, resource use, latency, availability, and reliability; create alerts and remediation.
• Support GPU and high-performance computing; optimize compute, storage, networking, and cloud resources.
• Implement Infrastructure as Code; manage Docker and Kubernetes environments; build reusable platform components and self-service tools.
• Standardize development, testing, staging, and production environments; implement security controls and support model governance.
• Troubleshoot platform and deployment issues, conduct root-cause analysis, maintain runbooks, and support migration and modernization initiatives.

Requirements

• Bachelor’s degree in Computer Science, AI, Machine Learning, Software Engineering, Data Engineering, or a related field.
• Previous experience in MLOps, ML Engineering, AI Platform Engineering, DevOps, Cloud Engineering, or a similar role.
• Strong understanding of ML lifecycle management and production ML systems; hands-on experience deploying and operating production models.
• Experience with an MLOps platform such as MLflow, Kubeflow, Airflow, SageMaker, Vertex AI, Azure Machine Learning, or equivalent.
• Strong experience with AWS, Azure, or Google Cloud; Docker, Kubernetes, Helm, and containerized workloads.
• Knowledge of CI/CD, Git, DevOps, Infrastructure as Code, and programming or scripting with Python, Bash, Go, or similar.
• Understanding of ML frameworks, model serving, APIs, inference infrastructure, monitoring, drift, distributed systems, and cloud architecture.
• GPU or accelerated computing experience is advantageous; relevant cloud, Kubernetes, AI, ML, or MLOps certifications are advantageous.
• Troubleshooting, debugging, root-cause analysis, documentation, communication, collaboration, and problem-solving skills.

Tools and technologies

• MLOps tools may include MLflow, Kubeflow, Airflow, SageMaker, Vertex AI, or Azure Machine Learning.
• Infrastructure and observability tools may include Terraform, CloudFormation, Pulumi, Prometheus, Grafana, or Datadog.
• Machine-learning frameworks may include PyTorch, TensorFlow, or Scikit-learn.

What the employer offers

• Competitive salary and employee benefits; opportunity to work with AI platforms, cloud infrastructure, GPU technologies, and MLOps tools.
• Professional development and support for relevant certifications; collaborative engineering environment and opportunities to lead platform initiatives.