
Enterprise Machine Learning Architect
13 hours ago
This position requires a highly skilled Machine Learning Infrastructure Engineer to design and implement scalable data pipelines to support enterprise machine learning systems.
The ideal candidate will have experience in transforming offline models into production-ready machine learning systems, developing and deploying tools and services for machine learning training and inference, evaluating new technologies to improve performance, maintainability, and reliability of machine learning systems, applying software engineering rigor and best practices to machine learning, including CI/CD, automation, etc., supporting model development with a focus on auditability, versioning, and data security, facilitating the development and deployment of proof-of-concept machine learning systems, communicating with clients to build requirements and track progress, analyzing structured, semi-structured, and unstructured datasets using advanced skills, and applying Machine Learning techniques such as Decision Trees, Random Forest, Boosting Algorithm, Neural Networks, Deep Learning, Support Vector Machines, Clustering, Bayesian Networks, Reinforcement Learning, Feature Reduction.
Key Responsibilities:- Design and implement scalable data pipelines to support enterprise machine learning systems.
- Transform offline models into production-ready machine learning systems.
- Develop and deploy tools and services for machine learning training and inference.
- 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 a focus 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.
- Analyze structured, semi-structured, and unstructured datasets using advanced skills.
- Apply Machine Learning techniques such as Decision Trees, Random Forest, Boosting Algorithm, Neural Networks, Deep Learning, Support Vector Machines, Clustering, Bayesian Networks, Reinforcement Learning, Feature Reduction.
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