Senior AI Engineer

SoFi • San Francisco, CA

7d

About The Position

SoFi’s AI Specialist - GenAI, NLP (Banking/Financial Services) is a critical hands-on engineer position in SoFi’s growing independent risk organization focussed on applying data processing/reporting and practical artificial intelligence techniques to solve real world problems. This role will be instrumental in conceptualizing, prototyping and implementing best-in-class AI-based solutions to meet risk management requirements.
This hands-on individual contributor role will work closely with the Director of Risk Analytics, and will play a pivotal role in developing data, reporting, and infrastructure solutions supporting the risk function. This is a crucial role for the independent risk function as we execute our mission to help more members get their money right.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Data Science, AI, Machine Learning, or a related field. PhD is a plus.
• 3+ years software development experience, with 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 experience in developing agentic solutions, including familiarity with tool-use, planning, memory, and reflection patterns.
• Deep understanding of Large Language Model (LLM) architectures, prompt engineering, retrieval-augmented generation (RAG), and advanced text generation techniques.
• Direct experience implementing AI observability solutions (e.g., using tools like LangSmith, Arize, Weights & Biases) and establishing rigorous tracing and testing methodologies for LLM workflows.
• 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.
• Expertise in AI system evaluation, including selection and application of appropriate performance metrics across diverse scenarios.
• 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 Haves

• 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.

Responsibilities

• Architect AI Solutions: Design and develop AI-based solutions leveraging available Generative AI (Gen AI), BERT based LLMs, and natural language processing to enable enhanced risk reporting, deeper insights, and automated risk management web applications and solutions.
• Develop Agent Systems: Serve as a subject matter expert in developing sophisticated agentic solutions utilizing Large Language Models (LLMs) to automate complex tasks and workflows.
• Implement AI Operations & Observability: Implement comprehensive AI observability solutions, including real-time monitoring, error tracking, and performance logging for deployed models.
• Model Optimization: Implement and manage parameter-efficient fine-tuning (PEFT) techniques (e.g., LoRA) to customize and optimize pre-trained models for specific tasks with minimal computational overhead
• Cross Functional Collaboration: Coordinate with cross-functional teams to distill specific requirements, project roadmaps, and ensure accurate and on-time project deliveries
• Proof of Concepts & Proposals - Identify areas for process enhancements and automation to streamline workflows and increase productivity within the risk management function.
• 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 This Job Offers

• Job Type: Full-time
• Career Level: Mid Level