Description :
- Design the data pipelines and engineering infrastructure to support our clients 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 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.
- 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.
- Communicate with clients to build requirements and track progress.
Qualifications :
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.Total Experience : 4+ years.
Education Qualification : (ref : hirist.tech)