.Play'n GO is one of the largest entertainment suppliers of online slots to the worldwide casino industry.Our Play'n GO pioneers were the first in the industry to recognise and develop the potential of gaming on the GO.At Play'n GO, if your talent and passion for excellence are anything like ours, you're going to grow here.Our strong growth is thanks to talented people like you who are inspired to help each other succeed.Connect with us and help us create entertainment that lasts.Job DetailsHours: Full TimeLocation: Fully remote.This opportunity is open to you if you can legally work remotely from one of our home countries in the USA, the UK, Sweden, Malta, Hungary, Spain, Poland, or the Philippines.Contract: PermanentAt Play'n GO we enjoy a flexible freedom to shape our own days. Everyone we hire is unique and every role we fill has certain performance expectations. The day-to-day challenges of work and life will be unique to you but our commitment to helping you find a balance will never vary.What the role entails:Some of the key responsibilities of our Generative AI Solution Engineer will be:Data collection and preprocessing:Identify all data sources required by the system, including documents, databases, web pages, etc.Define data requirements for data engineers who will set up the proper data pipelines.Ensure the data is clean and free from errors and irrelevant information.Enrich data with enough background information for the LLMs to learn.Ensure proper semantic representations of (numeric) tables.Extract meta data when needed.Data chunking:Consider the nature of the data and choose the best chunking approach accordingly.Creating and storing knowledge embeddings:Explore different embedding models and select the best performing one.Work with DB engineer to choose the right DB to store the knowledge embeddings.User queries processing:Transform the queries prior to retrieval.Convert transformed queries into an embedding.Retrieve the most relevant vectorized knowledge chunks.Post-retrieval processing:Summarize or re-rank the retrieved information.Generating Responses:Feed the combined queries and information chunks to LLMs.Implement the mathematical aggregation of multiple cells required by data analysis.Post-generation processing:Implement response quality checking.Maintain the history of a conversation if needed.What we ask of you:Experience of designing, developing and implementing LLMs based conversational AI products in cloud platforms, such as AWS Bedrock.Hands-on experience of RAG, LLM fine-tuning, advanced prompt engineering and handling LLM hallucinations.Strong technical proficiency with Python, SQL and AWS.Able to write production quality code and have adequate version control skills.Strong understanding of DevOps practices and CI/CD pipelines.Experience of closely working with data scientists and data engineers.If you feel great, you'll do great