Founding Engineer

🏦 Founded in 2024 by ML Engineers from Google, Polytechnique Paris, Centrale Paris, and a former Investment Banking Associate, DeepFlows is a rapidly growing tech company specializing in automating time-consuming tasks for Financial Advisors & Investors.

👨‍💻Our SaaS-application leverages cutting-edge AI technologies to strongly accelerate the M&A and PE processes.

💪Our clients already use the application on two uses cases co-developed with Investment Banks.

📈Our tool will revolutionize the way they conduct investment processes, while saving them huge amount of time and money: a win-win partnership with DeepFlows. We now aim at becoming an All-in-one application for Financial Advisors and Investors by developing our first use cases both in France and abroad, while keep developing co-identified functionalities!

As a Founding Engineer, you will be responsible for maintaining and enhancing the web platform, working closely with the co-founders, product managers, and UX designers. You will also serve as a crucial link between cutting-edge AI technology and its real-world application in investment processes. Your work will almost immediately be deployed to production, so you'll get to see the impact of your work.

You will be joining DeepFlows's Tech team, one of the first to be hired, and destined to grow strongly over the next two years. Your main focus will be to help DeepFlows become the market leader by building an all-in-one application with the best-in-class AI products. First, clients will mainly be M&A and Private Equity, quickly the application will also be deployed for Asset Management, Wealth Management, etc.

🔹 AI Model Development – Build & optimize AI agents, LLMs, RAG using PyTorch, LangChain, Hugging Face, Azure OpenAI Service.

🔹 MLOps & Infrastructure – Automate AI pipelines, monitor model drift with Azure Machine Learning (Azure ML), Docker, Kubernetes (AKS), MLflow.

🔹 Data Engineering – Scale financial AI data pipelines using Pandas, FAISS, Neo4J, PySpark, Azure Synapse Analytics, Azure Cognitive Search.

🔹 AI Strategy & Research – Align AI with investment use cases, focusing on NLP, LLM Optimization, Retrieval-Augmented Generation (RAG), Azure AI Services.

🔹 Technical Leadership – Guide the team, share AI knowledge using Jupyter, GitHub, Azure DevOps, Internal Docs.

• Degree in computer science, software engineering, or related field, or equivalent experience.
• At least 2 years of professional experience in Machine Learning Engineering and aware of the latest GenAI evolutions.
• Proficient in designing intricate software and transitioning them to production.
• Proactive and capable of working independently, yet thrive in collaborative team environments.
• Mastery of the MLOps technical stack, ensuring seamless integration of machine learning models into production pipelines.