Associate AI Engineer, Finance Transformation

SoFi is a next-generation financial services company and national bank using innovative, mobile-first technology to help members reach their financial goals. The Associate AI Engineer will build agentic AI workflows, telemetry, and experience layers that transform Finance processes while validating AI outputs for explainability, auditability, and accuracy. The role also involves rapidly prototyping prioritized use cases and partnering with AI platform teams to move solutions into production.

What you'll do:

• Develop multi-step AI workflows such as planning, tool use, retrieval, structured orchestration on approved enterprise AI platforms and agentic coding tools to automate Finance processes
• Own usage, cost, engagement, and ROI reporting across Finance AI tools, from source extraction to dashboards leadership relies on
• Help design how Finance users interact with AI systems including workflows, interfaces, and feedback loops that build trust and adoption
• Apply and refine repeatable patterns for validating AI outputs before they inform Finance decisions such as reconciliation, documented assumptions, human-in-the-loop checkpoints
• Turn prioritized Finance use cases into working prototypes, then partner with AI SDLC and platform engineering on the path to production
• Develop Finance fluency (close, reconciliation, reporting workflows) and SQL rigor in seat, with structured support

What they're looking for:

• Bachelor's or Master's degree in Computer Science, Data Science, AI/ML, or a related field, or equivalent practical experience
• 1–3 years of experience building with LLMs or an exceptional new-grad record with substantial hands-on agent projects (academic, personal, open-source, or internship work all count)
• Demonstrated hands-on experience with LLM APIs, prompt engineering, RAG, or agent frameworks — with a portfolio of projects where you can clearly explain what you personally designed and why
• Working Python for automation, API integration, and prototyping
• Basic SQL for querying data (Finance-grade rigor will be developed in seat)
• Builder mindset: bias toward making a working thing rather than describing one
• Willingness to learn Finance domain context and communicate with non-technical stakeholders, with support
• Exposure to evaluation, tracing, or observability practices for AI systems
• Exposure to Snowflake, Cortex, or natural-language-to-SQL tooling
• Exposure to Finance, Accounting, or another controlled/regulated data environment
• Experience building user-facing tools or internal apps powered by AI