CTO Late Co-Founder
- context
- DeepFlows
- Key Responsabilities & Tech Stack
- Qualification & Required Skills
CTO Late Co-Founder
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Founded in 2024 by ML & Soft engineers from Google, Polytechnique, Centrale Paris, and a former investment banker, DeepFlows is a fast-growing technology company that automates tedious analytical work for founders, financial advisors and investors. Backed by private investors from J.P. Morgan, Morgan Stanley, Meta, and others, and backed by Microsoft, the platform leverages cutting-edge AI agents to radically accelerate and improve decision-making in M&A, private equity and asset management.
| Domain | Key Missions |
| AI Pipeline Dev. | Design, build, and optimise a multi-agent orchestration layer (CrewAI → LangChain), plug-and-play LLM back-ends (Azure OpenAI, Amazon Bedrock), Retrieval-Augmented Generation pipelines (vector + knowledge-graph RAG in Neo4j), and a full evaluation/observability loop |
| MLOps & Infra | Own end-to-end CI/CD for models and services; containerise with Docker, run at scale on AKS/EKS, and govern experiments via Azure ML + MLflow. Stand-up AWS pathways for Bedrock-native models and future hybrid workloads |
| Data & Knowledge Engineering | Ingest and transform high-volume financial data using PySpark and Pandas; power low-latency search with FAISS vectors and Azure Cognitive Search; manage graph relationships in Neo4j and analytics in Azure Synapse |
| Security & Compliance | Unqualified SOC 2 Type II audit; zero major pen-test findings |
| AI Product Strategy | Translate fundraising and investor workflows into NLP/LLM solutions; drive model-selection, prompt-tuning, RAG/KG innovation, and cost-to-accuracy optimisation; maintain a forward-looking tech radar for emerging foundation models |
| Leadership & Culture | Set engineering standards, run design reviews, and foster knowledge-sharing through Linear, GitHub, Azure DevOps, and living internal docs; mentor a high-performance team and recruit top-tier AI talent |
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Degree in computer science, software engineering, or related field, or equivalent experience;
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At least +4-6 years of professional experience in Machine Learning Engineering and deep hands-on expertise with LLMs, retrieval-augmented generation, and knowledge graphs;
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Proactive and capable of working independently, yet thrive in collaborative team environments;
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Comfortable interfacing with customers, investors, and the board;
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Bonus: Published research or open-source contribution in NLP . Experience with Finance/Investment firms. Prior success in a seed to series B startup journey.