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# Senior Backend Engineer, Agentic AI

- **Company:** Airwallex US, LLC
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Salary:** $150,000.0 - $245,000.0
- **Contract:** Permanent contract
- **Skills:** Computer-Aided Design, Application Programming Interfaces (APIs), Artificial Intelligence, BigQuery, Data Architecture, Database Design, Distributed Systems, Python (Programming Language), NoSQL, Standard Sql, Web Services, Backend, Fastapi, Event Driven Architecture, Virtual Agents, Microservices
- **Published:** June 19, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=00964632eba1b2bd

## About the Role

Do you have experience in System security?, * 5+ years backend experience with Python at scale.

 * Deep knowledge of SQL/NoSQL database design.
 * Experience with distributed systems, event-driven architectures, or microservices.
 * Familiarity with GCP services (Pub/Sub, Cloud Run, Cloud SQL, BigQuery, KMS, etc.).
 * Strong grasp of security and compliance best practices.
 * Passion for building the backend that powers intelligent products.

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## Description

As a Senior Backend Engineer, you'll design the backbone of our intelligent finance systems: scalable services, resilient APIs, and thoughtful data models. Our backend is primarily Python on GCP, and your work will ensure we can move quickly while maintaining enterprise-grade reliability and security.

What You'll Do

 * Design and operate distributed, event-driven systems that process high-volume, high-sensitivity financial data with strict correctness and latency guarantees.
 * Build typed, production-grade APIs and services in Python (FastAPI + Pydantic) that power agentic workflows, AI copilots, and real-time financial infrastructure.
 * Own data architecture end-to-end schema design, event models, ledger primitives, and storage layers optimized for auditability, traceability, and scale.
 * Develop resilient infrastructure primitives (queues, retries, idempotency, observability, backpressure handling) that let teams ship fast without sacrificing reliability.
 * Work at the intersection of systems + AI, building feedback loops where product data continuously improves models and models autonomously improve system behavior.
 * Ship platform foundations, not one-off features internal frameworks, SDKs, and tooling that accelerate every engineer in the org.
 * Partner closely with ML + product teams to productionize research-grade ideas into real customer-facing systems.
 * Help define the architecture of an AI-native financial stack not bolting AI onto software, but designing software assuming AI is a first-class runtime.


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