Manager - Gen AI, NLP (Banking/Financial Services)

The role:

SoFi's Manager - 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 Engineering & AI, and will play a pivotal role in developing data, reporting, and infrastructure solutions supporting the risk function and enabling innovation via cutting-edge data engineering and AI techniques. 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:

AI Solution Development: Design and develop AI-based solutions leveraging available Generative AI (Gen AI) LLMs and/or natural language processing as applicable, to enable enhanced risk reporting, conversational risk analysis/commentary, and automated risk management processes

• Data Handling and Preprocessing: Work with large structured/unstructured data sets, performing data sourcing, preprocessing, tokenization, and feature extraction to prepare data for Gen AI adoption.
• Model Adoption: Design, develop, and optimize RAG (Retrieval-Augmented Generation) on available LLMs integrated with vector databases to develop solutions for specific use cases to optimize output accuracy and effectiveness, ensuring enhanced user experiences.
• Cross Functional Collaboration: Coordinate with cross-functional teams to distill specific requirements, project roadmaps, and ensure accurate and on-time project deliveries
• Solution Performance Monitoring: Periodically assess solution performance ensuring they meet applicable performance, compliance, and security standards. Implement retraining and continuous improvement strategies.
• 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 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 5+ years of hands-on experience in AI/ML with a focus on Generative AI, Large Language Models, and NLP, preferably in the banking or financial services domain.
• Proven experience in developing and deploying production-grade GenAI and NLP solutions for risk management, document understanding, fraud detection, or compliance.
• Programming Languages: Proficiency in Python is required, with knowledge of Java, Scala, or C++ as a plus.
• LLM/GenAI Technologies: Export experience with frameworks like OpenAI GPT, GPT-3, GPT-4, Codex, or similar LLM platforms (e.g., Google's PaLM, Meta's LLaMA, Anthropic's Claude, or custom fine-tuning on Hugging Face).
• NLP Libraries: Proficiency in NLP libraries such as Hugging Face Transformers, SpaCy, NLTK, Gensim, and OpenNLP.
• Data Engineering: Experience with large-scale data handling, including unstructured text processing, tokenization, embeddings (e.g., Word2Vec, BERT, or Transformer-based models), and data pipelines.
• Cloud Platforms: Experience with cloud-based machine learning and AI platforms such as AWS (SageMaker, Lambda) and Snowflake with a focus on GenAI model training, deployment, and monitoring.
• MLOps and Deployment: Hands-on experience in deploying machine learning models using CI/CD pipelines, containers (Docker, Kubernetes), and APIs for serving LLM/NLP models at scale.
• APIs & Microservices: Experience developing and integrating AI-powered APIs and microservices architecture into banking applications.
• Data Storage: Experience with relational and NoSQL databases (e.g., PostgreSQL, MongoDB) and data lakes for handling large amounts of text data.
• Search and Retrieval: Experience with vector databases and retrieval-augmented generation (RAG) techniques using systems like Elasticsearch, Pinecone, or FAISS for enhancing LLM performance.
• Strong analytical and problem-solving skills with attention to detail and an ability to work with complex, large-scale systems.
• Ability to communicate complex AI concepts and solutions clearly to non-technical stakeholders.
• Strong collaboration skills, with experience working in agile, cross-functional teams.
• 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.

Nice to have:

• Familiarity with regulatory frameworks and ethical considerations in AI within the banking industry (e.g., GDPR, data privacy, model explainability).