Shaping the future of banking with AI: Distilling and optimizing Large Language Models for AI assistants in finance and banking
The integration of financial services with technology has created an overwhelming amount of structured and unstructured data. To turn this into reliable real-time intelligence we need efficient and well-optimized language models not just raw scale.
Swissquotes Data Science team offers an internship focused on distillation fine-tuning and parameter-efficient adaptation (e.g. LoRA) of Large Language Models (LLMs) for production-grade AI assistants.
The main objective is to explore design and refine model compression and adaptation techniques to build fast cost-efficient and robust AI assistants tailored to the banking and finance domains.
The DS team is integrated in Swissquotes core research group the Quantitative Research & Solutions (QRS) department specialized in theoretical and applied research in Quantitative Finance and Data Science.
QRSs mission is to provide best-in-class quantitative and AI solutions by performing research to support every division of the bank in need of an analytical and data-driven approach.
By Joining The Team As Data Scientist Intern You Will :
- Prepare and curate financial text datasets for training fine-tuning and evaluation
- Apply and benchmark LLM distillation techniques to build smaller faster chatbot models
- Use LoRA and other parameter-efficient fine-tuning methods to adapt LLMs to banking-specific tasks
- Design experiments evaluate model performance and help integrate optimized models into AI assistant pipelines
Qualifications :
- Solid background in data science and NLP
- Prior exposure to or a strong interest in LLMs fine-tuning and/or model compression
- Coding proficiency in Python (PyTorch / TensorFlow or similar is a plus)
- Interest in the banking and financial sectors
- Creative and curious comfortable questioning existing approaches
- Organized self-motivated with excellent communication skills
- Ability to work in a team and communicate with different stakeholders
- Fluent in English
Remote Work :
No
Employment Type :
Full-time
Shaping the future of banking with AI: Distilling and optimizing Large Language Models for AI assistants in finance and bankingThe integration of financial services with technology has created an overwhelming amount of structured and unstructured data. To turn this into reliable real-time intelligen...
Shaping the future of banking with AI: Distilling and optimizing Large Language Models for AI assistants in finance and banking
The integration of financial services with technology has created an overwhelming amount of structured and unstructured data. To turn this into reliable real-time intelligence we need efficient and well-optimized language models not just raw scale.
Swissquotes Data Science team offers an internship focused on distillation fine-tuning and parameter-efficient adaptation (e.g. LoRA) of Large Language Models (LLMs) for production-grade AI assistants.
The main objective is to explore design and refine model compression and adaptation techniques to build fast cost-efficient and robust AI assistants tailored to the banking and finance domains.
The DS team is integrated in Swissquotes core research group the Quantitative Research & Solutions (QRS) department specialized in theoretical and applied research in Quantitative Finance and Data Science.
QRSs mission is to provide best-in-class quantitative and AI solutions by performing research to support every division of the bank in need of an analytical and data-driven approach.
By Joining The Team As Data Scientist Intern You Will :
- Prepare and curate financial text datasets for training fine-tuning and evaluation
- Apply and benchmark LLM distillation techniques to build smaller faster chatbot models
- Use LoRA and other parameter-efficient fine-tuning methods to adapt LLMs to banking-specific tasks
- Design experiments evaluate model performance and help integrate optimized models into AI assistant pipelines
Qualifications :
- Solid background in data science and NLP
- Prior exposure to or a strong interest in LLMs fine-tuning and/or model compression
- Coding proficiency in Python (PyTorch / TensorFlow or similar is a plus)
- Interest in the banking and financial sectors
- Creative and curious comfortable questioning existing approaches
- Organized self-motivated with excellent communication skills
- Ability to work in a team and communicate with different stakeholders
- Fluent in English
Remote Work :
No
Employment Type :
Full-time
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