About the company
SoFi is a next-generation financial services company and national bank using innovative, mobile-first technology to help millions of members reach their goals.
Responsibilities
- Define the long-term technical architecture and strategy for the next-generation AI platform, focusing on robust, scalable agentic frameworks and LLM deployment patterns
- 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
- Develop robust, persistent infrastructure for agentic state management, ensuring long-running agent workflows maintain context and reliability across distributed nodes and regional failovers
- Pioneer and institutionalize advanced parameter-efficient fine-tuning (PEFT) and compression techniques to maximize model performance and minimize operational costs
- Support the development of a unified model serving platform designed to host internally fine-tuned and custom-trained models for high-throughput, low-latency inference across diverse hardware footprints
- Define and enforce high standards for AI operationalization, including AI observability solutions and advanced tracing/testing frameworks
- Mentor senior and junior AI Engineers, elevating the overall engineering quality
- Coordinate with cross-functional teams to distill requirements, project roadmaps, and ensure accurate and on-time project deliveries
- Stay up-to-date with the latest trends and advancements in GenAI, LLMs, and NLP
Requirements
- 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 deploying production-level AI applications
- 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 advanced use of planning, memory management, tool integration, and control flow
- Deep understanding of 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)
- Deep expertise in building or extending inference engines (e.g., vLLM, NVIDIA Triton, or TGI) and managing Kubernetes/GPU orchestration
- Deep experience designing and institutionalizing AI observability solutions (e.g., LangSmith, Arize, Deepchecks) and advanced tracing and testing methodologies
- 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 RAG techniques using systems like Elasticsearch, Pinecone, or FAISS
- Exceptional ability to communicate complex technical concepts, drive consensus among senior technical leaders, and influence organizational AI strategy
- Strong analytical and problem-solving skills
- Strong collaboration skills, with experience working in agile, cross-functional teams
Conditions
- Location: CA - San Francisco