AI Security Engineer

Job Title: AI Security Engineer

Location:

[Location] (Opportunities for remote/hybrid/flexible work available)

Reports to:

Head of Security, CISO, Head of Engineering, CTO depending on organisation

Role Purpose

We are seeking a skilled AI Security Engineer to join our growing team. As part of an organization dedicated to creating cutting-edge artificial intelligence solutions, you will play a central role in building and safeguarding the integrity of our AI products. This position can be based in [Location], with flexible or remote work options for qualified candidates. The primary objective is to anticipate and mitigate threats to our AI-driven systems, ensuring the protection of valuable data, models, and intellectual property at every stage of development and deployment.

Company Overview

[Company Name] is an innovative organization specializing in advanced AI technologies for the [Industry] sector. Our core values emphasize collaboration, integrity, and the practical application of machine learning to solve real-world challenges. We have been recognized for our welcoming culture, commitment to professional growth, and dedication to delivering reliable AI solutions. By joining our team, you will collaborate with talented individuals who share a passion for quality, security, and the responsible use of artificial intelligence in a constantly changing market.

Key Responsibilities

• Architect Secure AI Systems: Design and implement robust security frameworks for AI and machine learning models, ensuring data confidentiality, integrity, and availability.

• Threat Detection and Response: Develop strategies to identify, evaluate, and respond to potential risks associated with adversarial attacks, data poisoning, model inversion, and other AI-specific threats.

• Model Hardening and Validation: Conduct experiments to reinforce models against attempts at unauthorized access or manipulation, collaborating with data scientists and ML engineers to enhance robustness.

• Secure Data Handling: Establish procedures for secure data ingestion, storage, and processing, including anonymization methods to protect sensitive datasets used in training and inference.

• Cloud Security Integration: Work closely with DevOps and cloud engineering teams to embed security controls into CI/CD pipelines, container orchestration, and microservices in cloud environments (AWS, Azure, GCP).

• Incident Management: Investigate AI-related security incidents, perform root-cause analysis, and provide clear documentation of remediation steps.

• Regulatory Compliance: Align AI security strategies with relevant standards (ISO 27001, SOC 2, HIPAA, GDPR, and others) while keeping records of compliance activities.

• Security Education: Serve as an internal advisor for best practices in AI security, collaborating with cross-functional teams to increase awareness and instill secure development habits.

• Continuous Improvement: Stay updated on emerging threats, vulnerabilities, and protective techniques in the AI and cybersecurity domains, adjusting security measures as needed.

Required Skills and Qualifications

• Education: Bachelor’s or Master’s degree in Computer Science, Cybersecurity, Electrical Engineering, or related field. Comparable professional experience is also acceptable.

• Security Fundamentals: Comprehensive understanding of encryption, authentication, secure coding practices, and network protocols.

• AI/ML Knowledge: Experience with machine learning frameworks (e.g., TensorFlow, PyTorch) and an understanding of model lifecycles (data collection, training, validation, deployment).

• Adversarial ML Experience: Awareness of common adversarial techniques (e.g., evasion, poisoning, model extraction) and best practices for threat mitigation.

• Cloud Proficiency: Hands-on familiarity with at least one major cloud platform (AWS, Azure, or GCP), including IAM, network configuration, and container security.

• Regulatory Compliance: Working knowledge of standards like ISO 27001 and GDPR. Ability to translate regulatory requirements into actionable security controls.

• Programming and Scripting: Proficiency in languages like Python, Go, or C++, as well as automation through scripting (Bash, Python).

• Communication and Teamwork: Strong written and verbal communication skills, with the ability to partner across engineering, data science, and product teams.

• Certifications (Preferred): CISSP, CISM, CEH, or other relevant security certifications that demonstrate expertise.
   

Preferred Experience and Attributes

• MLOps Security Integration: Experience embedding security checks within automated pipelines, working with tools like Jenkins, GitLab CI, or GitHub Actions.

• Penetration Testing for AI: Familiarity with specialized tools and techniques for testing AI models and data pipelines.

• Data Privacy and Governance: Knowledge of methods to manage and catalog data securely, ensuring compliance with internal and external requirements.

• Automation at Scale: Comfort with infrastructure-as-code (Terraform, Ansible) and monitoring tools (Prometheus, Grafana) to streamline security measures.

• Adaptability: Strong drive to remain updated on new AI and cybersecurity developments, refining processes as technology advances.

• Collaborative Mindset: Ability to partner effectively with a diverse range of teams, from research scientists to product managers, to align security goals with organizational priorities.

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Perks and Benefits:

Clearly outline the benefits and perks of the role.

How to Apply:

End with a strong call to action encouraging candidates to apply. Include a direct link to the application page and provide contact information for further queries.

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