AI Engineer

The role:

SoFi’s AI Engineer is a hands-on engineering role within SoFi’s growing independent risk organization, focused on building agentic AI systems to solve real-world, high-impact problems. This role will be instrumental in designing, prototyping, and deploying AI systems that enhance risk management and internal workflows.

This role sits at the intersection of the intelligence layer, including LLMs, agents, and orchestration, and the experience layer, which focuses on how users interact with and derive value from the AI systems developed. You will work closely with the Senior Manager of AI Engineering within the Risk Analytics group to build systems that are technically strong, intuitive, reliable, and impactful for end users.

What you’ll do: 

• Architect and Develop Agentic AI Systems: Design, build, and orchestrate 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.
• Design the Experience Layer: Work closely with stakeholders to define how users interact with AI systems, including designing intuitive workflows, interfaces, and feedback loops that drive adoption and trust.
• Context Engineering and System Design: Structure inputs, outputs, and system context to improve reliability and performance of LLM systems, including prompt design, retrieval strategies, and workflow composition.
• Productionize AI Systems: Develop production-grade services and APIs, integrate agents into real systems, and ensure scalability, reliability, and maintainability.
• AI Observability and Evaluation: Build tracing, debugging, and evaluation frameworks to understand system behavior and continuously improve agent performance.
• Cross-Functional Collaboration: Partner with risk, engineering, and business teams to translate ambiguous problems into working AI systems and deliver measurable outcomes.
• Proof of Concepts and Innovation: Identify opportunities to automate workflows using AI, rapidly prototype solutions, and evaluate new tools and approaches by staying up-to-date with latest trends in AI

What you’ll need:

• Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, or a related field.
• Two to five years of software development experience, with hands-on experience building and shipping AI-powered applications or workflows.
• Experience working with LLMs and building applications using prompting, APIs, or agent frameworks.
• Familiarity with agentic patterns such as tool use, multi-step reasoning, or workflow orchestration.
• Experience structuring inputs and outputs for LLM systems through context engineering, including prompt design and retrieval-based approaches.
• Experience building backend services and APIs, with Python preferred.
• Familiarity with cloud platforms such as AWS, Azure, or GCP and modern development practices.
• Experience working with data, including structured or unstructured data, and building pipelines for downstream applications.
• Understanding of how to evaluate AI systems, including defining success metrics and iterating based on performance.
• Strong problem-solving skills and the ability to work through ambiguous problems.
• Strong communication and collaboration skills, with the ability to work cross-functionally.
• An ownership mindset with a bias for action in building and shipping solutions.

Nice to have:

• Experience with frameworks for building agentic applications such as LangGraph.
• Experience designing user-facing workflows, internal tools, or applications powered by AI.
• Familiarity with observability tools for AI systems such as Langfuse / LangSmith
• Exposure to financial services or risk-related use cases.
• Experience with frontend technologies such as React for building AI-powered interfaces.