MX is a financial technology company on a mission to empower the world to be financially strong. We build the data infrastructure and intelligent solutions that help leading banks, credit unions, and fintechs deliver smarter, more personalized financial experiences to millions of consumers every day.

Operating at the intersection of scale and agility, MX combines the momentum of a market-proven platform with the speed, innovation, and ownership of a growth-stage company. Here, thoughtful execution matters, innovation is expected, and team members have a direct seat at the table to influence products and outcomes.

Our culture is built on curiosity, accountability, and measurable impact. We give people the autonomy to question assumptions, build better systems, and shape how we scale. If you’re driven to tackle complex technical and business challenges alongside a collaborative team that builds with sane, high-trust execution, you’ll thrive at MX.

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.