Senior Developer - Quantitative

Charles Schwab Inc.•Southlake, TX

1d•$58 - $67•Onsite

About The Position

Schwab Technology Services (STS) enables the future of how clients manage their money by providing innovative and reliable technological products and services as a part of our ongoing commitment to democratize access to investing and financial planning. Schwab Asset Management Solutions (SAMS) Technology is part of the STS organization which is responsible for the company’s use of information technology including all telecommunications, operations and client and business applications. SAMS Technology is aligned to support the technology needs of SAMS which is the investment advisor for Schwab’s proprietary mutual funds, referred to as the Schwab Funds; and it includes Schwab’s exchange-traded funds, referred to as the Schwab ETFs™.

Requirements

Bachelor’s degree in Computer Science, Computer Engineering, Data Science, Financial Engineering, or equivalent experience.
Proficiency in Python or any object-oriented design technologies.
Experience with quantitative technologies and AI tools and capabilities
Ability to independently prototype, evaluate, and iterate on solutions in ambiguous problem spaces.
Excellent oral and written communication, presentation, and facilitation skills.
Strong analytical and problem-solving skills.

Nice To Haves

Master’s degree in Computer Science, Computer Engineering, Data Science, Financial Engineering, or related field.
Experience with GitHub, GitHub actions for DevSecOps, and Atlassian suite of products (Confluence, JIRA, Bamboo) is preferred.
Experience with quantitative technologies and optimizers like Gurobi is highly preferred.
Financial Services Industry experience with exposure to portfolio management.
Experience with building solutions using Large Language Models (LLMs), Prompt Engineering, Fine-Tuning (LoRA, PEFT), In-Context Learning Orchestration & Frameworks: LangChain, LangGraph, LlamaIndex, Hugging Face Transformers AI Pipelines: RAG (Retrieval-Augmented Generation), Multi-Agent Systems, Embedding strategies

Responsibilities

Hands-on development, and participation in architectural designs.
Design, build, test, and deploy generative AI-enabled applications and services in an Agile practice.
Write production-quality code, prompts, evaluation scripts, and integration components.
Integrate generative AI initiatives into established enterprise SDLC processes.
Design and implement solutions leveraging large language models (LLMs), embeddings, retrieval-augmented generation (RAG), and conversational AI frameworks.
Build secure integrations between AI services and enterprise systems.
Develop reusable foundational components, such as prompt frameworks and reusable templates, RAG pipelines and retrieval orchestration layers and guardrails, validation, and output verification layers.
Optimize model usage for cost, latency, reliability, and scalability.
Implement human-in-the-loop validation and structured feedback capture mechanisms.
Lead troubleshooting and resolution of AI-related production issues.
Diagnose failures related to prompts, retrieval pipelines, integrations, and model behavior.
Drive automation of repetitive AI workflows and development processes.
Make independent technical judgment on architecture, tooling, deployment models, and SDLC enhancements for generative AI systems.
Contribute to AI-related technical decisions across product teams and engineering leadership.
Design AI systems that enhance internal productivity and improve user and business partner experiences through reliability, transparency, and measurable value delivery.