Product Management
GenAI Solution Architect
Content
from 23 €/hour.
Standort
Munich/Remote
Stunden Pro Woche
16-20 hours/week
You should bring this
Studies in the field of Computer Science, Business Information Systems, Economics, or a comparable qualification
Initial experience in product management or a strong affinity for it
Knowledge in the area of market research and/or customer feedback analysis
Good knowledge of Excel and PowerPoint
Good command of English, both spoken and written
An independent working style and the ability to learn quickly
Study of computer science (or a similar field) or simply a lot of experience through side projects
Advanced programming skills in Python and practical experience with NLP tools
Practical experience in building NLP applications, information retrieval systems, or question-answer pipelines
Good understanding of concepts, architectures, and evaluation metrics of retrieval-augmented generation
Experience with one or more LLM/RAG frameworks such as LangChain, LlamaIndex, or Semantic Kernel
Experience in collecting, cleaning, and structuring unstructured text data from various sources
Strong communication skills to effectively collaborate with technical and non-technical stakeholders
Your tasks with us
Support in identifying and analyzing market trends and customer needs
Assistance in creating product roadmaps and concepts
Collaboration with our team in implementing product strategies
Opportunity to implement your own ideas and projects
Participation in regular meetings and workshops
As a GenAI Solution Architect, you are responsible for the development of state-of-the-art question-answer pipelines – for the entire process from data extraction/processing to validation of results.
Collect and prepare data from various sources such as SharePoint, databases, APIs, and websites to build tailored knowledge bases.
Implement LLM pipelines using frameworks such as LangChain or LLamaIndex to enable chatbots to access and utilize the knowledge bases for generating informative responses (RAG).
Design and develop end-to-end conversational AI systems that leverage fine-tuning, RAG, and prompt engineering to deliver accurate, relevant, and contextual responses to user queries.
Establish testing and validation workflows for RAG systems to ensure high accuracy of the generated responses.
Monitor, analyze, and continuously improve the performance of RAG models in production environments.
Research and track the latest developments in the fields of RAG and conversational AI to introduce new capabilities and enhance the chatbot user experience.