Miami, FL US | Fully Remote TELECOMMUTE US
We are recruiting ML DevOps/ MLOpsan to join a company best descrbed as an elite team of AI practitioners who build state-of-the-art AI solutions to solve problems for the world’s biggest companies. You will learn more in our candidate interview guide.
About the job
Building the machine learning production infrastructure (or MLOps) is the biggest challenge most large companies currently have in making the transition to becoming an AI-driven organization.
This position is an opportunity for an experienced, server-side developer to build expertise in this exciting new frontier.
You will be part of a team deploying state-of-the-art AI solutions for enterprise clients. For example, suppose data scientists create an innovative solution for automatically reading and processing thousands of documents for one of the world’s largest banks. The solution works brilliantly in a development environment, but how should it be deployed into production? How will end users access the solution? How will it scale to processing millions of documents? What tools or platforms should the client use for monitoring?
An ML DevOps engineer needs to answer these questions AND build out the solution.
- Experience building end-to-end systems as a Platform Engineer, ML DevOps Engineer, or Data Engineer (or equivalent)
- Strong software engineering skills in complex, multi-language systems
- Fluency in Python
- Comfort 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
- Exposure to deep learning approaches and modeling frameworks (PyTorch, Tensorflow, Keras, etc.)
Education & Experience
2–5 years experience building production-quality software.
Bachelors or Masters degree and/or equivalent professional experience