Machine Learning Engineer

Description

• Must haves: NLP, conversational AI, Python

Key Responsibilities

• Understand the intent portfolio for NLU across domains (e.g., technology, human resources) and how it maps to conversation design for web, mobile, and voice channels.
• Identify and build appropriate datasets to train and test machine learning models for intent classification and speech recognition.
• Develop tools and telemetry that can measure/monitor accuracy and performance and update the models accordingly throughout development lifecycle.
• Develop disambiguating and error handling strategies as the virtual assistant scales.
• Monitor conversations in the application to identify underperforming content and develop solutions to improve the performance.
• Collaborate with data scientists, product owners, UX researchers, and engineers to build out the “brain” of the virtual assistant.
• Experience with conversational interfaces and natural language processing.
• Experience training machine learning algorithms for data classification and/or speech recognition.
• Experience improving intent recognition of a data classification model.
• Experience with Python.
• Unique skillset in computational linguistics and technical experience.
• Familiarity with LLMs.
• Familiarity with using version control technologies such as git, svn, or JIRA.
• Experience in DevOps and Agile methodology.