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Engineering

Mid DevSecOps

Secure the AI/ML pipelines that data scientists and engineers actually depend on to ship models into production.

Role details

Secure the AI/ML pipelines that data scientists and engineers actually depend on to ship models into production.

You'll own the security layer across the full AI/ML lifecycle, from model development through to deployment. That means building and maintaining CI/CD pipelines in tools like Jenkins, GitHub Actions, or GitLab, and making sure security is baked in at every stage. You'll sit at the intersection of DevOps, cloud security, and machine learning operations.

The team works across major cloud platforms, so you'll get real exposure to AWS, Azure, or GCP security tooling depending on the project. Containerisation is central to the work, with Docker and Kubernetes used throughout. Infrastructure as code is standard practice here, with Terraform and Ansible in the mix. If you've worked with MLOps frameworks like Kubeflow or MLflow, you'll feel at home quickly.

This is a mid-level role, so you'll have genuine scope to develop your AI security skills without being thrown in the deep end unsupported. The work is technically varied. One week you might be running vulnerability assessments on model infrastructure, the next you're working through compliance requirements with a privacy team. With 2-4 years of experience expected, there's room to grow into more senior responsibilities as the practice matures.

What You'll Do

  • Build and maintain secure CI/CD pipelines for AI/ML workflows using Jenkins, GitHub Actions, or GitLab.
  • Integrate security controls across model development, training, and deployment phases, including monitoring and data pipeline protection.
  • Conduct vulnerability assessments across AI models and infrastructure, addressing AI-specific risks like adversarial attacks and model poisoning.
  • Work alongside data scientists, AI engineers, and IT teams to maintain compliance with security and privacy standards.

What You'll Need

  • 2-4 years in a DevSecOps or related role, with hands-on CI/CD pipeline experience.
  • Cloud security knowledge across AWS, Azure, or GCP, plus solid Docker and Kubernetes experience.
  • Familiarity with MLOps frameworks such as Kubeflow or MLflow, and experience securing ML pipelines.
  • IaC experience with Terraform or Ansible, and working knowledge of security compliance frameworks.

About the Company

They're a global professional services firm operating at significant scale across technology, consulting, and managed services. The team works on complex, enterprise-grade projects for large clients, giving you exposure to varied environments and technical challenges you won't find in smaller settings.

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