We're looking for a Backend Engineer who writes clean, reliable code and thinks in systems. What you build here has a direct line to how financial professionals work - correctness and performance are not optional.
What you'll do
Design and build core backend services and APIs, including multi-tenancy, authentication, role-based access control (RBAC), data isolation, rate limiting, and usage tracking.
Build and maintain financial data pipelines for ingestion, normalization, validation, and time-series storage of market, fundamental, and macro data.
Build backend infrastructure for AI-powered products, including LLM streaming, tool execution, agent workflows, context/session management, and per-client usage tracking.
Build research and execution infrastructure, including backtesting services, SDKs, execution runtimes, background jobs, job orchestration, and sandboxed environments.
Design systems that handle high-concurrency and data-intensive workloads reliably.
Improve platform performance, observability, fault tolerance, and scalability as XNO grows.
Own the services you build end-to-end - from architecture and implementation to deployment, monitoring, and continuous improvement.
What we're looking for
Strong hands-on experience in backend/
software engineering, ideally building production systems in fintech, trading, data platforms, AI products, SaaS, or other data-intensive environments.
Strong Python or Golang - we use both.
Experience building production APIs with FastAPI or similar backend frameworks.
Solid experience with PostgreSQL: schema design, indexing, query optimization, transactions, and connection management.
Strong understanding of API design, including versioning, backward compatibility, authentication, and clear error semantics.
Good understanding of async/concurrency and distributed systems, including caching, queues, retries, idempotency, and failure handling.
Experience with platform-level problems such as multi-tenancy, RBAC, rate limiting, background jobs, message queues, or observability.
Experience building or integrating LLM/AI systems, such as streaming responses, tool calling, RAG, agent workflows, context management, or usage/cost tracking.
Comfortable taking ownership of a service or feature from design to production.
Bonus points
Experience with financial market data, including tick data, OHLCV, order book, or high-cardinality time-series data.
Experience building backtesting, trading, or quantitative research systems.
Experience with Kafka, Redis Streams, RabbitMQ, or similar messaging systems.
Experience with multi-tenant SaaS, including tenant isolation, quotas, permissions, and usage metering.
Experience with sandboxing and execution isolation, including containerization, resource limits, and circuit breakers.
Experience with TimescaleDB or similar time-series databases.
Experience with AI/agent frameworks such as LangChain, LangGraph, Langfuse, LangSmith, or similar tools.
Experience with Docker and cloud infrastructure.
Knowledge of or strong interest in quantitative finance and financial markets.
Benefits
Competitive salary + performance bonus.
Full ownership of the services you build - no ticket queues, no feature factory.
Work on challenging problems across financial data, backend systems, AI infrastructure, and quantitative research.
Direct collaboration with quant researchers, ML/
AI engineers, data engineers, and trading engineers on live systems.
Opportunity to build the core platform infrastructure of XNO as the company and its products scale.
Clear growth path toward Senior / Lead Software Engineer.