I
Inetum
Data Engineer

AI/ML Data Engineer

On-siteMidData Engineerposted 4mo ago
✦Role summaryAI-generated

This role bridges data engineering and cybersecurity by designing scalable pipelines for security event processing, ensuring seamless integration with SOC/ITSM tools while optimizing incident response workflows with AI-driven automation. The ideal candidate will balance technical depth in data infrastructure with domain expertise in security operations to enhance production-grade AI/ML systems.

Skills required

About this role

We are looking for an AI/ML Data Engineer to join a team focused on transforming data and incident response processes into a robust, secure, and production-ready AI system. This role combines data engineering, automation, and cybersecurity, with a strong impact in a production environment.

Key Responsibilities:

  • Develop and operate data pipelines (ingestion, normalization, and enrichment) for security events (logs, alerts, and tickets)
  • Implement end-to-end integrations with SOC and ITSM tools, ensuring data consistency and quality
  • Support the incident lifecycle (triage, investigation, and response) through AI-assisted models and automated playbooks
  • Optimize response processes by reducing operational noise and improving incident prioritization
  • Implement and evolve automation and orchestration workflows (SOAR)
  • Ensure MLOps/AIOps practices, including versioning, performance monitoring, and drift detection
  • Guarantee governance, security, and compliance in the use of data and AI models
  • Mitigate risks associated with AI usage (e.g., prompt injection, data leakage, incorrect outputs)
  • Collaborate with vendors and internal teams on the integration and operation of AI solutions
  • Contribute to technical and strategic decision-making, including risk and ROI analysis

Qualifications

  • Strong experience in Python (data engineering, automation, and integration)
  • Solid knowledge of SQL / PostgreSQL
  • Experience with data pipelines and log/event processing
  • Knowledge of ML/Deep Learning frameworks (e.g., PyTorch, TensorFlow)
  • Familiarity with versioning (Git), logging, and observability practices
  • Knowledge of access control and authentication (SSO, SAML, LDAP)
  • Experience or ability to work with data and ML infrastructure in production environments

Nice to Have:

  • Experience with SIEM, SOAR, and EDR
  • Experience with high-availability environments and infrastructure (including GPU)
  • Knowledge of MLOps/AIOps and AI governance

Soft Skills:

  • High level of autonomy and critical thinking
  • Ability to work in complex, production-oriented environments
  • Strong sense of ownership and attention to detail, especially in security contexts
  • Good communication and collaboration skills with both technical and business teams
  • Continuous improvement mindset and focus on operational efficiency

Additional Information

  • Workplace type: Hybrid (max. of 3 times per week in the office).
  • Location: Picoas, Lisboa.
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