Senior Manager, AI Engineer

SoFi-posted 17 days ago

Full-time • Senior

New York City, NY

1,001-5,000 employees

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SoFi’s Senior Manager, 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.

Technical & Execution Leadership: Serve as the technical authority for AI engineering Independent Risk Management projects, leading teams and initiatives from ideation through successful production deployment to meet critical stakeholder needs.
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.
Thought Leadership in Agentic Systems: Drive the research, design, and implementation of cutting-edge, mission-critical agentic solutions, establishing best practices and pushing the boundaries of what our systems can achieve.
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.
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.
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.

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.
Deep experience designing and institutionalizing AI observability solutions (e.g., LangSmith, Arize, Deepchecks) and advanced tracing and testing methodologies for LLM and agentic systems.
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.
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.

Familiarity with regulatory frameworks and ethical considerations in AI within the banking industry (e.g., GDPR, data privacy, model explainability).
Experience in banking or financial services use cases such as conversational AI for customer service, intelligent document processing for loan applications, fraud detection, or risk analysis.

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