Lead AI Security Engineer

Our Purpose

Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential.

Title and Summary

Lead AI Security Engineer

Overview
Mastercard is seeking a Lead AI Security Engineer to support a strategic AI engineering team focused on foundation model development and AI use case delivery. This role is responsible for the hands-on implementation of model security, AI product platform security and responsible-AI practices. Testing models for vulnerabilities, building the guardrails, access controls, API security, secure data usage and mitigations recommended by Mastercard's central security and AI governance teams, and ensuring those recommendations are actually built into the platform, not just documented. This is an execution-focused engineering role: Mastercard's dedicated security and governance teams already own policy, advisory guidance, and formal review. This role is the team's technical owner for turning that guidance into working, tested implementation.


Role
In this role, you will be responsible for implementing and maintaining the technical controls, tests, and mitigations that keep the platform's models secure and its AI use responsible in practice. Along with ensuring our developers, data engineers and AI engineers are incorporating security standards into their builds.

Key responsibilities:

Implement the technical recommendations produced by central security and AI governance review teams. Translating advisory guidance into concrete engineering work, and tracking it through to completion.
Test models for security vulnerabilities directly. Running practical assessments for risks such as membership inference, model inversion, data leakage through embeddings or outputs, and adversarial input manipulation.
Build and maintain guardrails and mitigations identified through vulnerability testing or governance review. For example, output filtering, input validation, rate limiting on sensitive queries, or access restrictions tied to specific risk findings.
Implement bias and fairness testing for models and use cases, working from criteria and guidance set by central AI ethics/governance teams
Build technical evidence and test results to support governance and security reviews. Providing concrete, reproducible proof (test results, logs, benchmark outputs) that central governance teams need.
Stay current on emerging model vulnerability research and attack techniques (e.g., new inversion or extraction methods relevant to embeddings and transformer-based models) and proactively test the platform against them.
Partner closely with central security, privacy, and AI governance teams as the team's primary technical point of contact. Understanding their requirements accurately and representing the platform's architecture to them clearly.
Advise engineering peers on secure design choices during development, providing practical, specific input on API, data pipeline, and model-serving designs before they reach formal review.
Support incident response for security or model-vulnerability findings, including reproducing issues, implementing fixes, and verifying remediation.

All About You
Required skills and experience:

Hands-on experience testing ML/AI models for security vulnerabilities. Adversarial robustness testing, membership inference, model inversion, or similar practical red-teaming of models, not just familiarity with the concepts.
Strong applied security engineering skills. Able to build and implement real technical controls (guardrails, filters, access restrictions), not only assess or advise on them.
Experience implementing bias/fairness testing or mitigation for ML models.
Familiarity with the unique security risks of embeddings and foundation models specifically. How they differ from traditional application security risks, and awareness of current research/attack techniques in this space.
Practical experience with access control and audit logging implementation, particularly across distributed or hybrid (cloud and on-premises) environments.
Working knowledge of relevant regulatory context for financial/payment data (e.g., PCI DSS, data protection regulation) sufficient to implement controls correctly.
Software engineering fundamentals. Able to build production-quality tooling and tests, not just one-off scripts or proofs of concept.
Clear, precise communicator, able to work effectively with both hands-on engineering peers and external specialist/advisory teams.
Cloud platform familiarity (AWS preferred; Azure or GCP valuable), particularly within a modern data/AI platform such as Databricks.
Comfortable working from external guidance and translating it into engineering work. Takes direction from specialist governance/security partner teams while retaining the technical judgment to implement it correctly for this platform's specific architecture.

Corporate Security Responsibility

All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:

• Abide by Mastercard’s security policies and practices;

• Ensure the confidentiality and integrity of the information being accessed;

• Report any suspected information security violation or breach, and

• Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.