The internship is in the field of AI, with a particular focus on Generative AI, more particularly applied to the Banking/Financial and Insurance industries.
Participate in projects for banking and insurance clients, including workshops. Prepare client data and market data.
Develop AI models (Supervised/Non supervised).
Develop Generative AI frameworks and Orchestration. Gain skills in the field of financial data.
Integrate APIs and Orchestration framework.
Develop front-end to run Generative AI Applications.
Gathering Data: You will collect data from a variety of sources, including financial transactions, customer records, and market data. This involves utilizing APIs to extract data from external sources and integrating it with your data pipeline.
Cleaning and Preparing Data: You will clean and prepare the data for analysis, including removing outliers, identifying missing values, and handling inconsistencies. This involves utilizing data cleaning techniques and applying data manipulation skills using Python and libraries like Pandas.
Building and Evaluating Models: You will build machine learning and Generative AI models to detect fraud, spot compliance issues, assess data quality... This may involve experimenting with various algorithms, hyperparameter tuning, and evaluating model performance using metrics like accuracy, precision, and recall.
Develop front-end to run Generative AI Applications, including text (LLM) and Image diffusion models.
Integrating Azure APIs seamlessly into the machine learning workflow to enhance data collection, model development, and deployment processes.
Communicating Results: You will communicate the results of your work to stakeholders, including clients, managers, and technical teams. This will involve presenting findings in clear and concise presentations, using visualizations and data storytelling techniques.
Ideal candidates are currently enrolled in the final year of engineering school or the equivalent university program (BAC +4/ +5) looking for an end of studies internship, or gap year (3 to 6 months starting around 08-09/2024), with a specialization in Data Science and AI. A good background in applied mathematics, statistics and machine learning is essential, along with proficiency in Python, SQL and API manipulation. Experience with Pandas, Keras, LLM, GPT, LangChain or LlamaIndex, Flask, JavaScript and Microsoft Azure is desirable.
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