AI Security Engineer

Job Description

REQUIREMENTS

• 3-5+ years in software engineering, ML engineering, or application security
• Hands-on experience with AI/ML systems — LLMs, NLP models, or similar
• Python proficiency for automation and scripting
• Experience working with Claude Code
• Strong understanding of cloud platforms: AWS, Azure, or GCP
• Experience with API security, Docker, Kubernetes
• Knowledge of AI-specific security risks and mitigations
• Experience conducting threat modeling and risk assessments

Preferred Qualifications:

• Familiarity with RAG architectures, vector databases, ML pipelines (MLflow, Kubeflow, SageMaker)
• Experience in fintech or regulated environments
• Knowledge of AI governance frameworks (EU AI Act, NIST AI RMF, ISO/IEC 42001)
• Experience with AI red teaming
• Background in cybersecurity or application security (OWASP, Secure SDLC)

Soft Skills:

• Strong analytical and problem-solving skills
• Ability to translate technical risk into business impact
• Able to explain AI security risks and mitigations to non-security teams
• Cross-functional collaboration with ML, data, and product teams
• Clear documentation and communication skills

RESPONSIBILITES

AI/ML Security Architecture

• Design and implement security controls for AI/ML systems across development, training, and production
• Secure LLM integrations, RAG pipelines, and AI APIs
• Conduct threat modeling for AI systems and data pipelines
• Define secure-by-design patterns for AI-powered features

AI Threat Detection & Mitigation

• Identify and mitigate AI-specific threats: prompt injection and jailbreak techniques, model poisoning and data contamination, adversarial attacks, training data leakage, insecure model serialization, excessive permissions in AI agents
• Develop guardrails, content filters, and output validation mechanisms
• Implement monitoring for anomalous AI behavior

Secure Development & DevSecOps

• Integrate AI security checks into CI/CD pipelines
• Perform security reviews of ML code and AI-related infrastructure
• Secure model registries and artifact storage
• Collaborate with other engineers and platform teams to enforce security standards

Data Protection & Compliance

• Ensure AI systems comply with: GDPR and data privacy regulations, financial industry regulatory requirements, implement controls for sensitive data used in training and inference, perform AI risk assessments aligned with internal risk methodology

Governance & Policy

• Contribute to AI security standards and internal policies
• Define AI risk classification and control frameworks
• Support security reviews for new AI initiatives / tools

Are you interested in this position?

Apply by clicking on the “Apply Now” button below!

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