Senior ML Engineer: Vulnerability Intelligence in Production

Cytidel; Ireland; On-site; EUR 90,000 - 130,000; Full time

Benefits offered by this job

Remote-first within Ireland

22 days annual leave+ holidays

Direct access to founders

Job summary

Cytidel is building the first dedicated ML-driven vulnerability intelligence platform. We’re looking for an ML engineer to own the ML function, set its technical direction, and collaborate with a team that has existing exploratory models and data pipelines.

You’ll help productionize models, build inference services, and monitor performance at scale. The role emphasizes Python backend work, ML production experience, and the use of LLMs as components within pipelines.

Qualifications

• 5+ years as an ML engineer with at least two production models you personally took from scoping to live to monitored.
• Strong Python and backend engineering: FastAPI, Docker, CI/CD as everyday tools.
• SQL fluent and comfortable pulling your own features from the warehouse.
• Comfortable using LLMs as components inside ML pipelines: embeddings, fine-tuning, classification, summarisation.
• Sharp evaluation instincts with a track record of regression discovery in production.

Responsibilities

• Own the ML function and define its technical direction.
• Collaborate with the team that built exploratory models and pipelines.
• Build production‑grade inference services and feature pipelines.
• Implement drift monitoring and retraining loops.
• Ship models and prioritisation logic into the product to support remediation decisions.

Skills

Python

Production ML

Backend development

SQL

LLMs in ML pipelines

Model evaluation & monitoring

Tools

FastAPI

Docker

CI/CD

SQL

Job description

Cytidel is building the first dedicated ML-driven vulnerability intelligence platform. We’re looking for an ML engineer to own the ML function, set its technical direction, and collaborate with a team that has existing exploratory models and data pipelines.

You’ll help productionize models, build inference services, and monitor performance at scale. The role emphasizes Python backend work, ML production experience, and the use of LLMs as components within pipelines.