AI/ML Security Specialist

K20s Kinetic Technologies; Dubai; On-site; AED 200,000 - 350,000; Full time

Job summary

A leading company in technology is seeking an experienced AI/ML Security Specialist to protect machine learning models and AI systems from emerging threats. The role involves designing comprehensive security measures, conducting risk assessments, and collaborating with data and security teams to ensure compliance with regulatory standards. Candidates should have a strong background in AI security and cybersecurity, with a focus on adversarial machine learning.

Qualifications

• Minimum 7 years experience in cybersecurity, with 3+ years in AI/ML security.
• Strong understanding of security frameworks and compliance standards.
• Familiarity with cloud AI platforms and security.

Responsibilities

• Design end-to-end security measures for AI/ML models.
• Conduct threat modeling and risk assessments for workflows.
• Collaborate with MLOps to integrate security into ML processes.

Skills

Machine learning algorithms

Deep learning frameworks

Adversarial ML frameworks

Python

Analytical skills

Education

Bachelor's or Master's degree in Computer Science

Tools

TensorFlow

PyTorch

AWS SageMaker

Azure ML

GCP Vertex AI

Job description

Job Title: AI/ML Security Specialist Model Protection & Adversarial Threat Defense
Location: United Arab Emirates
Experience: 7+ Years
Job Type: Long-Term | On-site
Department: Cybersecurity / AI Security / Data Science

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Job Summary

We are seeking an experienced AI/ML Security Specialist to secure machine learning models, training pipelines, and AI-enabled systems against evolving threats such as adversarial inputs, model inversion, and data poisoning attacks. The ideal candidate will work closely with AI, data science, and security teams to embed security controls throughout the ML lifecycle from data collection to model deployment while ensuring compliance with industry and regulatory standards.

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Key Responsibilities:

Design and implement end-to-end security measures for AI/ML models, data pipelines, and inference systems.

Identify and mitigate risks related to adversarial machine learning, data poisoning, model theft, and evasion attacks.

Conduct threat modeling and risk assessments for AI/ML workflows across cloud and on-premise environments.

Develop tools and techniques to test model robustness, perform attack simulations, and validate model integrity.

Secure data collection, annotation, and preprocessing stages to ensure data provenance and quality.

Implement model explainability, differential privacy, federated learning, and privacy-preserving ML techniques where applicable.

Collaborate with MLOps and DevSecOps teams to integrate security into AI/ML CI/CD pipelines.

Monitor emerging research in AI security and apply state-of-the-art techniques to defend against novel threats.

Prepare documentation and reports for compliance with regulatory standards (e.g., GDPR, NIST AI RMF, ISO/IEC 23894).

Train stakeholders and AI teams on AI security best practices and attack surface awareness.

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Required Skills & Qualifications:

Bachelor s or Master s degree in Computer Science, Artificial Intelligence, Cybersecurity, or a related field.

Minimum of 7 years of experience in cybersecurity, with 3+ years focused on AI/ML security.

Strong understanding of machine learning algorithms, deep learning frameworks (TensorFlow, PyTorch), and data science workflows.

Experience with adversarial ML frameworks (e.g., CleverHans, IBM ART, Foolbox, TextAttack).

Knowledge of cryptographic techniques, secure multiparty computation (SMPC), and homomorphic encryption is a plus.

Familiarity with cloud AI platforms (AWS SageMaker, Azure ML, GCP Vertex AI) and their security configurations.

Strong coding skills in Python and familiarity with ML toolkits and security libraries.

Preferred certifications: CEH, CSSLP, GPCS, GCPN, ML-specific SANS certs, or equivalent.

Excellent analytical, documentation, and communication skills to collaborate across technical and non-technical teams.

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Nice to Have:

Experience with AI governance, AI red teaming, or model auditing.

Knowledge of large language models (LLMs) and their security risks (e.g., prompt injection, data leakage).

Understanding of AI ethics, bias mitigation, and regulatory compliance frameworks.

Research or publications in AI/ML security conferences (e.g., Black Hat, DEF CON, NeurIPS, IEEE S&P).

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