Senior Engineering Manager - Risk

Mercury Mercury · Fintech · Remote · Software Engineering

Senior Engineering Manager to lead teams building AI/ML systems for risk detection, fraud detection, and application approvals in a fintech environment. The role involves applying both traditional ML and LLMs to improve real-time decisioning and user experience, with a focus on scaling these systems and ensuring operational excellence.

What you'd actually do

  1. Lead teams (4–8 engineers each) responsible for account onboarding, KYC/KYB, AML, and fraud detection decisioning and workflows, and operational tooling.
  2. Apply AI/ML—from traditional models to large language models—to unlock faster, real-time bank account application approvals. This work sits on the critical business path, directly driving efficiency and revenue growth.
  3. Partner with Product, Risk, and Data teams to design and deliver scalable systems that balance user experience with compliance rigor.
  4. Shape the next generation of our KYC and risk platforms—reliable, resilient, and easy to extend as regulations and business needs evolve.
  5. Create a strong culture of operational excellence, with measurable improvements to uptime, accuracy, and system quality.

Skills

Required

  • 9+ years of software development experience
  • 3–5+ years of engineering management in a high-scale tech environment
  • AI/ML expertise (building and launching applied AI products)
  • Experience with LLMs and traditional ML models
  • Experience building and scaling production AI systems (0→1 and 1→10)
  • Building large-scale backend distributed systems
  • Experience with KYC, AML, risk, or compliance systems
  • Raising the bar for quality and reliability
  • Strong communication and leadership skills
  • Hiring, retaining, and developing exceptional technical talent

Nice to have

  • Experience in financial services or fintech
  • Experience with integrations and decision automation
  • Curiosity about KYC, AML, risk, or compliance systems

What the JD emphasized

  • critical business path
  • AI/ML expertise—you’ve built and launched applied AI products (from LLMs to traditional ML models), shipping them from 0→1 and scaling 1→10 in production environments.
  • compliance rigor
  • operational excellence
  • balance shipping speed with technical excellence

Other signals

  • AI/ML for risk detection and decisioning
  • LLMs and traditional ML models
  • Real-time decision making
  • Building and scaling AI products