AI Engineer
Summary
Build and ship AI-powered features for a corporate expense management platform, integrating LLMs, RAG, and agent workflows to automate finance operations like expense categorization and policy enforcement.
About Us
SiFi is building the next-generation Office of the CFO — a corporate expense management platform that gives accounting teams seamless control over company spending. We let companies issue cards with precise spending restrictions, automate expense workflows, and close books faster. As we scale, AI is becoming core to how we deliver financial intelligence, automation, and trust to our customers.
We are a small, high-ownership team. Everyone builds, everyone ships, and everyone is accountable for outcomes.
About the Role
We are looking for an AI Engineer to join our Data & AI team and help us bring AI-powered automation into the heart of corporate finance. You will work across the full stack — from LLM pipelines and agent workflows to the backend infrastructure and internal tooling that makes them production-ready.
This is a hands-on, end-to-end role. You will own problems from prototype to production — in a domain where financial data is structured, high-stakes, and unforgiving. Getting it wrong has real consequences for real companies. You will work closely with Product, Engineering, Finance, and Operations to build AI features that make a direct impact on how hundreds of companies manage money.
What You'll Do
- Design and ship AI-powered features end to end — from prompt engineering and model integration to backend APIs and frontend surfaces.
- Build and maintain AI infrastructure for LLM inference, retrieval-augmented generation (RAG), and structured data extraction from financial documents
- Develop autonomous agent workflows that automate finance operations such as expense categorization, receipt reconciliation, and policy enforcement
- Partner with the Data Engineering team to ensure AI systems are grounded in clean, trusted, and well-modeled financial data.
- Build internal tooling and evaluation frameworks to measure, monitor, and improve model performance in production.
- Stay close to the state of the art and bring relevant techniques — fine-tuning, tool use, multi-agent orchestration — into SiFi's stack where they create real value
Requirements
Benefits
Why SiFi
- Work on AI problems that are genuinely hard: financial data is structured, high-stakes, and unforgiving — the opposite of toy problems
- Own meaningful surface area from day one on a lean team where your decisions shape the product.
- Competitive salary and equity
- Flexible working environment
- A front-row seat to building a category-defining fin-tech from the ground up