About this role
Description
Charm Security is building AI-powered defense systems that protect people from scams and social engineering attacks targeting financial institutions. Our platform combines real-time conversation analysis, behavioral science, and AI agents to break the scam spell before harm occurs.
Backed by Team8, we're deploying our technology with banks and financial services companies to protect their customers at the moment of risk. Our product suite includes a Prevention Agent for real-time customer protection, a Copilot for fraud analysts, and a Customer-Facing Agent for self-service fraud reporting.
This role spans our entire product stack. From React frontends through Node.js services to event-driven backends and the AI systems that power our fraud prevention.
Your Role
You’ll build the products that protect millions of banking customers from scams, working across our whole stack and shipping work that customers see and use.
- Own features end to end, from the first idea to production
- Build across the stack: user interfaces, APIs, real-time services, and the data underneath
- Design the systems that move fraud signals between our services and make our products respond live
- Turn our AI agents into reliable, polished product experiences
- Work with AI engineers, products, designers, and customers to decide what to build and how
- Help shape how we build as the team grows
Requirements
Requirements
- 5+ years of full stack development, with real depth on the backend
- Experience building real-time systems in production (WebSockets, streaming, or live data pipelines)
- Experience designing event-driven microservices for high throughput: queues/pub-sub, async workers, service-to-service contracts
- Strong TypeScript/Node.js, with solid API design
- Solid PostgreSQL and data modeling
- Comfort working across the stack: we use React on the frontend
- A lion mindset. We're at an early stage and we move fast.
Nice to Have
- Background in financial services, fraud prevention, or cybersecurity
- Experience building AI-assisted products or integrating LLMs
- Experience with Kubernetes and cloud-native observability