AI Engineer – Build AI‑powered finance automation

About the role

SiFi is seeking an AI Engineer to embed AI‑driven automation at the core of corporate finance. You will work across the full stack, from large‑language‑model (LLM) pipelines and autonomous agents to backend services and internal tooling, turning prototypes into production‑ready features.

Key responsibilities

• Design, develop, and ship end‑to‑end AI‑powered features, from prompt engineering and model integration to backend APIs and UI components.
• Build and maintain AI infrastructure for LLM inference, retrieval‑augmented generation (RAG), and structured extraction of financial documents.
• Develop autonomous agent workflows that automate finance operations such as expense categorisation, receipt reconciliation, and policy enforcement.
• Collaborate with the Data Engineering team to ensure AI systems rely on clean, trusted financial data.
• Create internal tooling and evaluation frameworks to monitor and improve model performance in production.
• Stay up‑to‑date with state‑of‑the‑art techniques and integrate fine‑tuning, tool use, or multi‑agent orchestration where they add value.

Required profile

• Strong engineering fundamentals in backend systems, APIs, and cloud infrastructure.
• Hands‑on experience building LLM‑based solutions in production (prompt design, RAG pipelines, structured extraction, or agentic systems).
• Comfort working with financial or compliance‑sensitive data where correctness and auditability are critical.
• A bias toward shipping, pragmatic trade‑offs, and rapid iteration based on real‑world feedback.
• Low ego, high ownership – comfortable debugging edge cases and proposing new architectures.

Required skills

• Backend development
• API design
• Cloud infrastructure (e.g., AWS, GCP)
• Large Language Model (LLM) integration
• Prompt engineering
• Retrieval‑augmented generation (RAG)
• Structured data extraction
• Agentic workflow development

What we offer

• Opportunity to solve genuinely hard AI problems on high‑stakes financial data.
• High‑ownership, small‑team environment where every contribution ships.
• Direct impact on hundreds of companies managing corporate spending.