Senior Staff AI Engineer

Employee Applicant Privacy Notice Who we are: Shape a brighter financial future with us. Together with our members, we’re changing the way people think about and interact with personal finance. We’re a next-generation financial services company and national bank using innovative, mobile-first technology to help our millions of members reach their goals. The industry is going through an unprecedented transformation, and we’re at the forefront. We’re proud to come to work every day knowing that what we do has a direct impact on people’s lives, with our core values guiding us every step of the way. Join us to invest in yourself, your career, and the financial world. The role: SoFi’s Senior Staff AI Engineer is a hands-on AI engineering role in SoFi’s growing independent risk organization. This is a critical, senior role responsible for setting the technical direction, driving execution, and ensuring the successful delivery of our most complex, production-level AI initiatives. This role will be instrumental in conceptualizing, prototyping and implementing best-in-class AI-based solutions to meet risk management and compliance requirements. This hands-on role will work closely with the Director of Risk Analytics, and will leverage your deep expertise to solve our hardest problems, mentor the next generation of engineers, and directly connect technical innovation to major business success. This is a crucial role for the independent risk function as we execute our mission to help more members get their money right. What you’ll do: • Architecture and Strategy: Define the long-term technical architecture and strategy for our next-generation AI platform, particularly focusing on robust, scalable agentic frameworks and LLM deployment patterns. • Advanced LLM Orchestration: Architect and standardize the use of graph-based LLM orchestration, leveraging expert-level mastery of LangGraph to solve highly complex, multi-stage reasoning problems at scale. • Distributed Agent Memory & State: Develop robust, persistent infrastructure for agentic state management, ensuring that long-running agent workflows maintain context and reliability across distributed nodes and regional failovers • Deep Model Optimization: Pioneer and institutionalize advanced parameter-efficient fine-tuning (PEFT) and compression techniques to maximize model performance and minimize operational costs across the organization. • Model Serving Infrastructure: Support the development of a unified model serving platform designed to host internally fine-tuned and custom-trained models to ensure high-throughput, low-latency inference across diverse hardware footprints. • Operational Excellence: Define and enforce high standards for AI operationalization, requiring mastery in designing and deploying comprehensive AI observability solutions and advanced tracing/testing frameworks that guarantee production quality, compliance, and reliability. • Mentorship: Mentor senior and junior AI Engineers, elevating the overall engineering quality • Cross Functional Collaboration: Coordinate with cross-functional teams to distill specific requirements, project roadmaps, and ensure accurate and on-time project deliveries • AI Innovation: Stay up-to-date with the latest trends and advancements in GenAI, LLMs, and NLP, evaluating and experimenting with new techniques and tools to push the boundaries of AI innovation in the banking sector. What you’ll need: • Bachelor’s or Master’s degree in Computer Science, Data Science, AI, Machine Learning, or a related field. PhD is a plus. • 8+ years software development experience, with 3+ years of hands-on experience in developing and successfully deploying production-level AI applications that have been used by real customers or internal stakeholders. • Expert-level experience with LangGraph to model and orchestrate complex, stateful multi-step reasoning and control flow in LLM applications. • Expert-level proficiency in developing sophisticated agentic solutions, with a portfolio demonstrating advanced use of planning, memory management, tool integration, and control flow. • Deep understanding of Large Language Model (LLM) architectures, prompt engineering, retrieval-augmented generation (RAG), and advanced text generation techniques. • Proven experience implementing parameter-efficient fine-tuning (PEFT) techniques (e.g., LoRA) to customize and optimize pre-trained models for specific tasks with minimal computational overhead. • Deep expertise in building or extending inference engines (e.g., vLLM, NVIDIA Triton, or TGI) and managing the underlying Kubernetes/GPU orchestration for custom model deployments. • Deep experience designing and institutionalizing AI observability solutions (e.g., LangSmith, Arize, Deepchecks) and advanced tracing and testing methodologies for LLM and agentic systems. • Experience with cloud platforms (AWS, Azure, or GCP) and containerization technologies (Docker, Kubernetes). • Expert level Python is required. • React is strongly preferred. • Experience with large-scale data handling, including unstructured and structured data pipelines, with a strong preference for Snowflake and DynamoDB. • Experience developing and integrating AI-powered APIs and microservices architecture into banking applications. • Experience with vector databases and retrieval-augmented generation (RAG) techniques using systems like Elasticsearch, Pinecone, or FAISS for enhancing LLM performance. • Exceptional ability to communicate complex technical concepts, drive consensus among senior technical leaders, and influence organizational AI strategy. • Strong analytical and problem-solving skills with attention to detail and an ability to work with complex, large-scale systems. • Strong collaboration skills, with experience working in agile, cross-functional teams. Nice to have: • Familiarity with regulatory frameworks and ethical considerations in AI within the banking industry (