MLOps Engineer

About this role

dv01 seeks an MLOps Engineer to build and operate the infrastructure that enables machine learning teams to train, deploy, and monitor models reliably in production. You'll own the ML lifecycle platform, CI/CD pipelines, monitoring systems, and cloud-native foundations while mentoring engineers and establishing governance practices for a fintech company serving 400+ financial institutions.

What you'll do

• Build and maintain ML lifecycle platforms using tools like MLflow for experiment tracking, model registry, and pipeline management
• Design and implement CI/CD pipelines that automate model deployment, including testing, containerization, and rollback capabilities
• Establish monitoring and observability for production models, tracking drift, latency, data quality, and cost with alerting systems
• Manage containerized workloads on Kubernetes and codify infrastructure using Terraform or similar IaC tools
• Implement governance, access controls, and deployment policies to ensure compliance with security and regulatory requirements
• Mentor junior engineers and define shared patterns that reduce friction for data science and application teams

What they're looking for

• ML lifecycle tooling (MLflow, Weights & Biases, Kubeflow, SageMaker, or Vertex AI Pipelines)
• Kubernetes and containerization (Docker)
• Infrastructure-as-code (Terraform, Pulumi)
• CI/CD pipeline design and automation
• Python and/or Go programming
• Cloud platforms (GCP preferred, others acceptable)
• Production system operations and monitoring
• Security practices (IAM, secrets management, infrastructure security)