Original listing text, shown exactly as published by the company.
Key Responsibilities
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.
Required Qualifications
- 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.
What you will get in return
- Competitive Salary: We believe great work deserves great pay! Your skills and talents will be rewarded with a salary that makes you feel valued and motivated.
- Work-Life Harmony: Join a company that genuinely cares about you - because your life outside of work matters just as much as your time on the clock. #LI-Hybrid
- Generous Time Off: Need a breather? Our annual leave policy lets you recharge and enjoy life outside of work without a worry.
- Employee Referral Program: Love working here? Share the love! Bring your talented friends on board and get rewarded for growing our awesome team.
- Comprehensive Health & Pension Benefits: From medical insurance to pension plans, we’ve got your back. Plus, location-specific benefits and perks!
- Workation Wonderland: Live your digital nomad dreams with 30 extra days to work remotely from anywhere in the world (some restrictions apply). Adventure awaits!
- Volunteer Days: Make a difference! Take two additional paid days each year to support causes you care about and give back to the community.
