Machine learning engineer

Machine Learning Ops Engineer Charlotte, NC (preferred)- Plano, TX- Richmond, VA- Atlanta, GA- Jacksonville, FL.- Must sit onsite at least 3 days a week. Top 3 requirements: Experience with Machine Learning and machine learning tools (Scikit-Learn and Deep Learning (Tensorflow or Keras) 4+ years working on python Cloud platforms- containers, OpenShift, Kubernetes, Bodman (deployments) 4+ years of using Jupyter/Eclipse/Spyder IDE Linux Bash scripting SQL

-ML- needs to know how to install platforms (going with a 3rd party vendor to bring on a new platform), Linux, understand OpenShift, Bodman, Understand Containers, Kubernetes Job Requirements: • Design the data pipelines and engineering infrastructure to support enterprise machine learning systems at scale • Strong understanding of machine learning concepts, have solid understanding of machine learning techniques, algorithms, and models • Proficiency in Software programming languages such as Python, as well as knowledge of software developments principles and best practices is essential • Familiarity with DevOps practices, including version control, CI/CD and infrastructure automation tools (e.g., Ansible) • Take offline models which data scientists build and turn them into a real machine learning production system • Develop and deploy scalable tools and services for our clients to handle machine learning training and inference • Identify and evaluate new technologies to improve performance, maintainability, and reliability of our clients’ machine learning systems • Support model development, with an emphasis on auditability, versioning, and data security • Facilitate the development and deployment of proof-of-concept machine learning systems • Communicate with teams to build requirements and track progress

Experience Required: • 4+ years’ experience building end-to-end systems as a Platform Engineer, ML DevOps Engineer, or Data Engineer (or equivalent) • 4+ years working on Python • 4+ years of using Jupyter/Eclipse/Spyder IDE • 4+ years of working on Linux environments • Experience working with Openshift, Kubernetes and container platforms • Experience building custom integrations between cloud-based systems using APIs • Experience developing and maintaining ML systems built with open source tools • Strong understanding of software engineering and CI/CD pipeline. • Familiarity with one or more data-oriented workflow orchestration frameworks (Airflow, Argo, etc.) • Ability to translate business needs to technical requirements • Exposure to machine learning methodology and best practices