We're looking for an experienced MLOps Engineer to support the consolidation and standardization of an enterprise MLOps platform. This role will focus on building secure, scalable model deployment pipelines and enabling machine learning models to be deployed across multiple serving environments.

What You'll Do

Design and implement automated model deployment pipelines Integrate ML models with Kubeflow, KServe, and other deployment platforms Develop reusable deployment templates, wrappers, and SDK extensions Standardize model serving, monitoring, and health check configurations Troubleshoot deployment, packaging, and performance issues Create technical documentation, runbooks, and deliver knowledge transfer to internal teams

What You'll Bring

✔ Strong Python development experience ✔ Expertise with Docker and Kubernetes (required) ✔ Experience implementing model serving solutions (REST APIs, gRPC) (required) ✔ Hands-on experience with model registries (Weights & Biases preferred) (required) ✔ Experience with Kubeflow, KServe, or Dagster (preferred) ✔ Familiarity with CI/CD tools (Jenkins, GitLab CI, ArgoCD) and AWS/Terraform is a plus

If you're passionate about building scalable MLOps infrastructure and deploying machine learning solutions in production, we'd love to hear from you!

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