MLOps Engineer

dv01 is a data analytics platform bringing transparency to structured finance. This MLOps Engineer role focuses on building and operating the ML lifecycle platform, CI/CD, and cloud-native infrastructure to get ML and AI workloads into production reliably.

• Build and operate the ML lifecycle platform, including experiment tracking, model registry, and versioned training/inference pipelines using MLflow or comparable systems.
• Own CI/CD and deployment for ML workloads, from packaging and containerization to automated testing, staged rollouts, and rollback.
• Make models observable and reliable in production with monitoring for latency, drift, data-quality, and cost.
• Manage Kubernetes-based containerized workloads and codify infrastructure with Terraform.
• Implement governance, access controls, deployment policies, and auditability for ML systems.
• Define repeatable patterns, provide technical mentorship, and contribute to MLOps practices.

• 4–7 years of experience in platform engineering, DevOps, or MLOps.
• Hands-on experience with ML lifecycle tooling (MLflow, Weights & Biases, Kubeflow, SageMaker, or Vertex AI Pipelines).
• Strong cloud-native infrastructure skills with Kubernetes and Terraform.