EY GDS Assurance Digital seeks an innovative Digital Manager to implement AI research for high-growth products. This role involves developing analytics-enabled solutions and integrating data science with business objectives to extract insights. You will collaborate with multi-disciplinary teams across regions to support global clients. This is a core full-time AI developer position, crucial for driving the innovation agenda through the application of AI techniques to solve business challenges.

- Translate business challenges into analytical problems and design solution approaches. - Clean, aggregate, analyze, and interpret data to uncover key business insights. - Lead the end-to-end AI/ML implementation process, including model design, feature planning, testing, production setup, monitoring, and release management. - Collaborate closely with Solution Architects on deploying AI Proofs of Concept (POCs) and scaling them into production-level applications. - Extract information from complex documents like PDFs, Word Docs, and forms, including entity extraction, table extraction, and information comparison.

This role requires an excellent academic background, with a minimum of a Bachelor's or Master's degree in Data Science, Business Analytics, Statistics, Engineering, Operational Research, or a related field with a strong emphasis on modern data architectures and processes.

A solid foundation in Python with advanced coding skills is essential. Candidates should possess over 6 years of core data science experience in areas such as Machine Learning (Regression, Classification, Decision Trees, Random Forests, Time Series Forecasting, Clustering), Deep Learning (DNN, RNN, LSTM, Encoder-Decoder Models), and Natural Language Processing (Text Summarization, Aspect Mining, Question Answering, Text Classification, NER, Language Translation, NLG, Sentiment Analysis).

Experience with Large Language Models (LLMs) like OpenAI's ChatGPT and GPT4, frameworks like LangChain and Llama Index, and concepts like Retriever Augmented Generation and prompt engineering is highly desirable. Familiarity with open-source LLM frameworks such as Mistral and Llama, including fine-tuning on custom datasets, is also a plus.

Proficiency in SQL/NoSQL databases, API deployment (Flask/FastAPI/Azure Function Apps), web app development, Docker, and Kubernetes is required. Additional skills include excellent communication, presentation, and facilitation abilities, demonstrated project management experience, and the capacity to coordinate multiple projects effectively through strong prioritization, organization, and self-discipline. Analytical thinking to solve complex problems and the ability to gather and analyze data from various sources are crucial.