Candidates will be evaluated based on their experience in building and testing Python applications.
Experience with infrastructure-as-code tools like ARM and Bicep templates for automating deployment processes.
Familiarity with deploying and managing machine learning models and APIs, particularly with OpenAI tools or similar frameworks, in production environments.
Experience with Azure Open AI services and managing cloud infrastructure.
Strong skills in scripting languages (e.g., PowerShell, Python, Bash).
Experience collaborating via Git.
Responsibilities:
Work with other machine learning scientists and engineers to develop custom ML solutions for a variety of industries and use cases.
Develop, train, and validate machine learning algorithms.
Guide client teams on implementing Azure Open AI services.
Build Azure infrastructure pipelines to create and deploy Azure services, specifically related to AU use cases.
Apply techniques such as classification, clustering, regression, NLP, deep learning, time series forecasting, and Bayesian methods to build scalable solutions.
Help create generalized solutions out of specific use cases and identify areas of opportunity and improvement within projects.
Research new techniques and technologies in ML and AI, evaluating their potential for business uses.
Collaborate in a fully remote, Agile-like environment using tools like Slack and Git.