Essential Duties and Responsibilities
- Develop and apply algorithms based on Natural Language Processing, Machine Learning (LLMs like GPT/ChatGPT), & Knowledge Graphs to extract, process & analyze information from unstructured documents
- Develop and apply algorithms for classifying, extracting, and redacting structured, semi-structured, and unstructured documents.
- Work in a team with data scientists, data & knowledge engineers, software developers, and product owners to create scalable, reusable and performant data-oriented applications. e.g., document understanding, chatbots, recommender, search
- Convey technically difficult concepts in a clear and understandable way, translate business needs to technical specifications, and provide guidance for applying natural language processing & machine learning to the management and sales team.
- Evaluate the latest methods and technologies and support the design of the data & analytics platforms and governance infrastructures
- Continuously improve quality and performance while owning applications from idea to operations
What You'll Need to Be Successful
- Bachelor’s degree in computer science, Engineering, or related field (or equivalent experience)
- At least 1 year of working experience in applying natural language processing in a business-relevant context, in production environments
- Expert understanding of Natural Language Processing, Machine Learning / Deep Learning as applied to Natural Language (e.g., LLMs)
- Experience in Intelligent Document Processing classification and extraction algorithms and techniques is a plus.
- Experience in any search (e.g., based on Elasticsearch, MongoDB), semantic technologies (e.g., Vector Databases), and chatbots is considered a plus - An independent, solution-oriented and team-oriented way of working, not afraid to take on responsibility
- Strong desire to learn & upskill at the intersection of data, science & industry Experience with the following is welcome and considered a plus
- Cloud technologies like Microsoft Azure (e.g., Databricks, Azure DevOps, Azure ML) - CI/CD using e.g., git, Jira, docker, Azure MLOps
- Agile software development using Scrum/Kanban