Real-Time Threat-Detection ML Engineer

Try&Hire

Genf

On-site

CHF 120,000 - 180,000

Full time

5 days ago
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Job summary

Try&Hire in Switzerland (Genf) seeks an experienced ML engineer to design and deploy scalable ML systems for real-time threat detection and anti-abuse protection. You will work on agentic AI systems, model development tooling, and explainability while collaborating with security analysts and frontend/backend engineers.

The role requires hands-on ML production experience, proficiency in Python, and a strong foundation in probability and experimental design.

Qualifications

  • Degree in Computer Science or a related quantitative field.
  • 2+ years hands-on experience building and running ML systems.
  • Production-level ML expertise with models at real-world scale.
  • Hands-on experience with LLMs in agentic environments.
  • Strong proficiency in Python and backend/server fundamentals.
  • Deep understanding of probability, hypothesis testing and experimental design.

Responsibilities

  • Design and deploy scalable ML systems with real-time inference and monitoring.
  • Architect agentic AI systems for autonomous threat detection and policy enforcement.
  • Build tools for model development, debugging and explainability.
  • Optimize ML workflows for distributed environments with large-scale data.
  • Collaborate with security analysts and backend/frontend engineers.
  • Advance ML techniques for anti-abuse and account security.

Skills

ML systems
Python
LLMs
Agentic AI
Backend fundamentals
Experimental design

Education

Degree in Computer Science or related field

Job description

Try&Hire in Switzerland (Genf) seeks an experienced ML engineer to design and deploy scalable ML systems for real-time threat detection and anti-abuse protection. You will work on agentic AI systems, model development tooling, and explainability while collaborating with security analysts and frontend/backend engineers.

The role requires hands-on ML production experience, proficiency in Python, and a strong foundation in probability and experimental design.

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