Internship - Data Support AI assistant - Industrialisation Phase

ABOUT CFM

Founded in 1991, we are a global quantitative and systematic asset management firm applying a scientific approach to finance to develop alternative investment strategies that create value for our clients.

We value innovation, dedication, collaboration, and the ability to make an impact. Together, we create a stimulating environment for talented and passionate experts in research, technology, and business to explore new ideas and challenge existing assumptions.

Internship Overview

As a continuation of the initial project focused on developing an AI Assistant for data-support enhancement at CFM, this internship presents an opportunity

to industrialize the proof-of-concept (POC) solution, leveraging AWS infrastructure.

The leading goal is to refine the AI system's application within asset management through benchmarking various large language model (LLM) options,

expanding the scope of data available, and integrating advanced features to enrich the user experience.

The selected candidate will work closely with cross-functional teams to ensure the assistant evolves into a robust tool that enhances operational

efficiencies.

Key Responsibilities

1. Industrialization of the POC:

2. Develop and deploy the existing AI Assistant solution on AWS, ensuring scalability and reliability.

3. Implement best practices for cloud architecture to optimize performance and cost-efficiency

4. Benchmarking LLM Models:

5. Conduct a comprehensive evaluation and benchmarking of different LLMs to identify the most effective model for the assistant's use

case.
- Analyze performance metrics, including response accuracy, speed, and resource utilization.

1. Scope Augmentation:

2. Collaborate with various teams across the company to expand the database, integrating additional data sources that enhance the AI

Assistant's capabilities.
- Ensure the assistant can cater to a broader range of use cases pertinent to different teams.

1. Issue Resolution:

2. Identify and troubleshoot issues such as AI hallucinations and refine the model to reduce errors.

3. Work on enhancing the frontend design of the assistant interface, ensuring a user-friendly experience.

4. Integration as a Feature:

5. Facilitate the seamless integration of the AI Assistant into the existing support dashboard to promote usage across teams.

6. Collaborate with the UI/UX team to create an intuitive interface that aligns with current systems.

7. Feature Exploration & Development:

8. Propose and prototype new features for the AI Assistant based on user feedback and specific business needs.

9. Explore the implementation of a human-in-the-loop mechanism to automate specific alerts resolution processes, enhancing the

assistant's utility.

1. Documentation and Training:

2. Document the industrialization process, including architecture, deployment procedures, and feature updates.

3. Prepare training materials and sessions for end-users, ensuring successful adoption of new functionalities.

Desired Skills and Qualifications

• Strong background in computer science, data science, or related fields.
• Experience in programming Python, SQL, and familiarity with AWS services.
• Experience with AI / LLMs.
• Knowledge of cloud architecture, including AWS tools like Lambda, S3, and SageMaker.
• Problem-solving skills and an attention to detail.
• Effective communication and collaboration abilities across multi-disciplinary teams.
• Learning Outcomes

EQUAL OPPORTUNITIES STATEMENT

We are continuously striving to be an equal opportunity employer and we prohibit any discrimination based on sex, disability, origin, sexual orientation, gender identity, age, race, or religion. We believe that our diversity, breadth of experience, and multiple points of view are among the leading factors in our success.

CFM is a signatory of the Women Empowerment Principles.

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• Strong background in computer science, data science, or related fields.
• Experience in programming Python, SQL, and familiarity with AWS services.
• Experience with AI / LLMs.
• Knowledge of cloud architecture, including AWS tools like Lambda, S3, and SageMaker.
• Problem-solving skills and an attention to detail.
• Effective communication and collaboration abilities across multi-disciplinary teams.
• Learning Outcomes