Staff AI Engineer

SoFi SoFi · Fintech · New York, NY · Risk 2LOD

Staff AI Engineer at SoFi, focused on designing, developing, and scaling agentic AI systems for risk management and internal workflows. This role involves architecting multi-step reasoning systems, designing user experience layers, context engineering, productionizing AI services, and building observability and evaluation frameworks. Requires strong backend engineering, LLM experience, and system design skills in a financial services context.

What you'd actually do

  1. Architect and Develop Agentic AI Systems: Lead the design and development of AI systems that leverage multi-step reasoning, tool use, and structured workflows, using frameworks such as LangGraph or similar approaches. Incorporate planning, memory, tool integration, and adaptive control flow to enable automated decisioning, risk insights, and internal platforms.
  2. Design the Experience Layer: Define how users interact with AI systems by designing workflows, interfaces, and feedback loops that drive adoption, usability, and trust. THis will involve close coordination with users / stakeholders. Ensure alignment between system behavior and user expectations.
  3. Context Engineering and System Design: Define and implement approaches for structuring inputs, outputs, and system context to improve reliability and performance of LLM systems, including prompt design, retrieval strategies, and workflow composition.
  4. Productionize AI Systems: Develop production-grade services and APIs, integrate agents into real systems, and ensure scalability, reliability, and maintainability.
  5. AI Observability and Evaluation: Build tracing, debugging, and evaluation frameworks to understand system behavior and continuously improve agent performance.

Skills

Required

  • 7+ years of software engineering experience
  • building and scaling AI-powered systems in production
  • working with LLMs and building applications using prompting, APIs, and/or agent frameworks
  • designing and implementing agentic systems
  • context engineering for LLM systems
  • prompt design
  • retrieval-based approaches
  • backend engineering
  • building scalable services and APIs (Python preferred)
  • cloud platforms such as AWS, Azure, or GCP
  • modern development and deployment practices
  • working with structured and unstructured data
  • building pipelines to support downstream AI applications
  • defining and implementing evaluation frameworks for AI systems
  • system design skills
  • architect scalable, reliable solutions
  • operate effectively in ambiguous problem spaces
  • translate them into well-defined systems
  • communication and collaboration skills
  • work cross-functionally
  • influence technical decisions
  • ownership mindset
  • delivering high-impact systems end-to-end

Nice to have

  • designing and building user-facing workflows or internal tools powered by AI
  • observability and evaluation tools for AI systems such as Langfuse, LangSmith, or similar
  • financial services or building systems for risk-related use cases
  • frontend technologies such as React for building AI-powered interfaces
  • contributing to shared platforms, libraries, or internal tooling that enable reuse across teams
  • building systems that require explainability, auditability, or operate in regulated environments

What the JD emphasized

  • agentic AI systems
  • agentic systems
  • multi-step reasoning
  • tool use
  • workflow orchestration
  • production-grade

Other signals

  • AI/ML is the role's core craft
  • ships ML models/agents/training/evals
  • design, development, and evolution of agentic AI systems
  • production-grade solutions
  • architecting, building, and scaling AI systems