Machine Learning Engineer

Description:

Role responsibilities:

Design the data pipelines and engineering infrastructure to support machine learning system 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 machine learning system.

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 system.

Communicate with Data Science team to build requirements and track progress.

Experience /Tech:

Experience building end-to-end systems as a ML Ops Engineer, or Data Engineer (or equivalent)

Strong software engineering skills in complex, multi-language systems

Fluency in Python, especially in one or more API frameworks (eg. FastAPI, Flask, Django)

Experience with Azure platform especially in Azure Machine Learning

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.

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 modelling frameworks (PyTorch, Tensorflow, Keras, MLFlow, etc.)