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Staff Backend SWE - Billing Platform
Syndesus
Worldwide$180k–220kRemote3w ago
- Employment
- Full-time
- Seniority
- Staff
About the role
- Designing and building the backend services that power a new AI-native billing platform, hosted on GKE and built cloud-native from the ground up
- Integrating LLMs, ML models, and external AI services into billing workflows — with proper observability, fallback behaviour, and human review where appropriate
- Identifying and implementing practical AI opportunities across the SDLC: summarization, extraction, classification, recommendation, and workflow automation
- Partnering closely with a principal frontend engineer and cross-functional teams (product, design, data, operations) to turn complex billing pain points into scalable solutions
- Leading code reviews, establishing engineering best practices, and mentoring junior and mid-level engineers
- Contributing to technical direction and architecture decisions for a platform with a real end-of-year launch target
- 8+ years of professional software engineering experience
- Strong, production-depth Java and Spring Boot — this is their core backend language and a hard requirement
- RESTful APIs and microservices architecture at scale
- Hands-on React and frontend fluency — you're comfortable owning frontend work, not just aware of it
- Cloud platform experience: GCP preferred, AWS or Azure also fine; you understand cloud-native architecture in practice
- Containers, Kubernetes, CI/CD pipelines (Jenkins, Harness, Docker) — not just awareness, actual production use
- Database depth across relational and NoSQL: MySQL, PostgreSQL, MongoDB, DynamoDB
- You use AI tools every day across your full workflow — research, debugging, code generation, deployment. This isn't a nice-to-have; it's how they work and how they expect this person to work
- Recent TypeScript or Node.js experience alongside your Java background — they're evolving ther stack and that combination is genuinely valuable there
- Prior exposure to US healthcare billing, claims processing, or RCM — the domain has a steep learning curve and prior knowledge meaningfully accelerates impact
- Experience building production AI-enabled services — agentic workflows, LLM integration, evaluation frameworks, not just using Copilot
- Track record of greenfield platform delivery with real deadlines and real stakes
Compensation
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