Product Security Architect

Introduction to the Position

The Product Security Architect champions secure development, application security, and the secure adoption of AI throughout the organization. Working with the Engineering and Product teams, the Security Architect ensures that applications are developed using secure patterns and reviews changes to existing applications. They also ensure that the underlying infrastructure, systems, and AI services are managed securely.

As a key member of the Information Security department, the Security Architect helps build a comprehensive suite of security capabilities, controls, and standards, and uses AI to scale and automate that work. In a nutshell: building security and automation into DevOps/SRE and engineering processes, and building security into how MX builds and uses AI.

Job Responsibilities

• Architect security systems and designs to help secure MX’s platform and products, including AI and LLM-powered features, both in the cloud and on-prem/colo.
• Mature the MX Security Architecture review program and processes, with review criteria for both traditional applications and AI systems (agents, RAG pipelines, model and tool integrations).
• Automate application security tooling by building it into CI/CD processes, using AI-assisted analysis to triage findings, reduce false positives, and suggest fixes to developers.
• Ensure that software is developed securely with resilient architectures and patterns, including guardrails for AI features that handle customer or financial data.
• Partner with the Platform and DevOps teams to implement appropriate security architecture through threat models, tooling, and automation for Kubernetes, AWS/GCP, and the AI services and models running on them.
• Establish security checkpoints in the SDLC to ensure that secure code practices are followed, including standards for secure use of AI coding assistants and review of AI-generated code.
• Write automation scripts, Terraform, custom SAST/IAST rules, and integration code to embed security controls directly into engineering workflows.
• Understand and track the current threat landscape for the products and software we develop, including AI-specific threats (OWASP Top 10 for LLM Applications, MITRE ATLAS), and create controls accordingly.
• Function as a subject matter expert on application security architecture and AI security architecture.
• Assess third-party software and AI vendors for security risk, and help shape MX's AI governance.
• Support security engineering initiatives by guiding and mentoring engineers across the security organization.
• Partner with stakeholders to mature a Security Champion program designed to both build in security concepts in our development flows, but also empower our development and engineering teams to self-identify and remediate findings traditionally driven by a ProdSec or AppSec engineer.

Basic Qualifications (Must-Have)

• Bachelor's degree in Computer Science, Information Security, or a related field (or equivalent practical experience).
• 12+ years of experience in Application Security, Application Architecture, DevOps, or a related field.
• Solid understanding of automation tooling, primarily CI/CD pipelines and containers, and experience using AI tools to automate or speed up security work.
• Solid understanding of computer security principles and development processes, including AI-specific threats and frameworks.
• Deep understanding of software dependencies, related vulnerabilities, and secure use of software repositories and open source software, extending to AI models, datasets, and AI-generated code.
• Knowledge of secure coding principles implemented in 2-3 languages and the ability to partner and collaborate with developers, including guiding the secure use of AI coding assistants.
• Excellent communication skills and the ability to engage with technical and non-technical departments and teams.

Preferred Qualifications

• Master's degree or PhD in Computer Science, Information Security, or a related field.
• Hands-on experience securing applications that use LLMs, ML models, or AI agents.
• Knowledge of financial industry regulations, such as SOC 2 and PCI.
• Experience in a regulated, fintech, or SaaS environment, and a track record of mentoring engineers or acting as a team lead.