Responsibilities
- Architecting, building and maintaining NLP processing pipelines / workflows in Python via technologies such as serverless functions, Airflow, MLflow, or other.
- Working with Data Engineers and Data Scientists to automate experiments that bring ML and NLP research ideas to production.
- Fine Tuning and consuming Large Language Models (LLM) directly or via third party Generative AI API services.
- Setting up Retrieval Augmented Generation (RAG) architectures, prompt engineering, and Reinforcement Learning from Human Feedback (RLHF) processes.
- Implement MLOps and related DevOps focused on LLMs deployed in cloud-native environments.
- Support, optimise and transition our current processes to ensure well architected implementations and best practices.
- Work in an agile environment within a collaborative agile product team using Kanban
- Collaborate across departments and work closely with data science teams and with business (economists/data) analysts in refining their data requirements for various initiatives and data consumption requirements.
- Participate in designing and building NLP pipelining and choosing data processing techniques, which make it easier for them to integrate and consume data needed for various use cases.
- Participate in ensuring compliance and governance during model use, to ensure that the users and consumers use the resources provisioned to them responsibly through data governance and compliance initiatives.
Work Arrangement
Hybrid
Team
Team size: 200. Structure: Technology team within The Economist Group, collaborating with corporate data team, data scientists, analysts, and business (economists/data) analysts across the organisation.
Additional Information
- Flexible working policy with remote first approach.
- Minimum expectation of coming to the office two days a month, but option to come in more often.
