Senior AI/ML Engineer (Stealth Fintech Cybersecurity Startup)

Description

Contrat : CDI

Localisation : Full Remote – Europe timezone preferred

Mission:  

We are assembling a core team for a European stealth startup operating at the convergence of AI, cybersecurity, and finance. Our ambition: to build zero-touch, LLM-powered cybersecurity infrastructure tailored for the financial sector — locally hosted, privacy-compliant, and capable of autonomous threat detection and mitigation.

We are looking for a Senior AI/ML Engineer with deep expertise in building and optimizing large language model (LLM)-based systems, preferably with exposure to secure, high-compliance environments.

What you’ll do

• Design and develop secure, local LLM-based components for threat detection, incident response, and risk profiling.
• Train and fine-tune open-source models (Mistral, LLaMA, Falcon, etc.) for narrow, domain-specific tasks (e.g., anomaly detection, log analysis, fraud signals).
• Build scalable pipelines for ingesting structured and unstructured data from logs, API streams, and financial systems.
• Collaborate with cybersecurity experts to integrate ML outputs into actionable defense workflows.
• Stay ahead of the curve in privacy-preserving machine learning, edge AI, and federated architectures.

What we’re looking for

• 5+ years of experience in machine learning, with at least 2 years working on LLMs or foundation models.
• Solid Python skills (PyTorch, HuggingFace, LangChain or similar).
• Experience with RAG pipelines, fine-tuning, quantization (ggml, vLLM, etc.), or serving local LLMs.
• Comfort with working in early-stage, fast-paced, ambiguity-rich environments.
• Bonus: familiarity with SIEM systems, threat intelligence platforms, or cybersecurity analytics.
• Bonus: prior experience in fintech or high-security environments (e.g., health, gov, defense).

Why join

• Greenfield project with long-term funding and European reach.
• Early team, high autonomy, zero bureaucracy.
• Cutting-edge stack: open-weight LLMs, modular micro-agents, synthetic data environments.
• Purpose-driven: reshaping cybersecurity infrastructure with AI-native principles.