Senior ML Ops Engineer

Proven experience in an MLOps, DevOps, or a related software engineering field Strong cloud infrastructure experience with AWS Deep understanding of Kubernetes Expertise with IaC (Infrastructure as Code) & GitOps Tools like Terraform and Argo CD Experience developing and augmenting CI/CD Pipelines Strong understanding of the state of the art in machine learning, especially LLMs Familiarity with ML frameworks such as PyTorch, MLFlow, vLLM, Transformers, and Torch Practical experience managing data quality and performance in production ML environments Experience designing data architectures optimized for AI/ML, such as with Opensearch (vector databases)

Operationalize ML and LLM-based workloads Create, manage, and improve continuous integration and delivery pipelines Implement observability practices for ML systems Maintain and improve the infrastructure for ML/LLM evaluation sets Ensure high data quality and monitor performance of ML workloads in production Work closely with ML engineers, data scientists, and cross-functional teams Participate in an on-call rotation