ML Ops Engineer

Profile

Requirements

• Experience building end-to-end systems as a Platform Engineer, MLOps Engineer, or Data Engineer (or equivalent).
• Hands-on expertise in Python and ML frameworks.
• Expertise with Linux administration.
• Experience working with cloud computing and database systems.
• Experience building custom integrations between cloud-based systems using APIs.
• Experience developing and maintaining ML systems built with open source tools.
• Experience developing with containers and Kubernetes in cloud computing environments.
• Familiarity with one or more data-oriented workflow orchestration frameworks (KubeFlow, Airflow, Argo, etc.).
• Ability to translate business needs to technical requirements.
• Strong understanding of software testing, benchmarking, and continuous integration.
• Exposure to machine learning methodology and best practices.
• Experience with Prometheus and Grafana integrations for highly scalable environments.

Responsibilities

• Design the data pipelines and engineering infrastructure to support enterprise machine learning systems at scale. 
• Take offline models data scientists build and turn them into a real machine learning production system.
• Develop and deploy scalable tools and services to handle machine learning training and inference.
• Identify and evaluate new technologies to improve performance, maintainability, and reliability of machine learning systems.
• Apply software engineering rigor and best practices to machine learning, including CI/CD, automation, etc.
• Support model development, with an emphasis on auditability, versioning, and data security.
• Facilitate the development and deployment of proof-of-concept machine learning systems.

Join the awesome squad. Apply now!