Senior AI Engineer- AI Engineering & Operations

Charles Schwab Inc.•Austin, TX

4d•$129,000 - $175,000•Onsite

About The Position

Schwab Technology Services enables the future of how clients manage their money by providing innovative and reliable technology products and services as part of our ongoing commitment to democratize access to investing and financial planning. As part of Schwab's AI Engineering & Operations team, you will be building the next generation Generative AI solutions that shape the future of technology at Schwab. In this role, you will contribute to the development and deployment of AI products that are instrumental in driving data-informed business decisions and elevating client experiences. You’ll collaborate across teams to deliver scalable, secure, and high-performing AI systems that align with Schwab’s innovation strategy and operational goals. We’re seeking a Senior AI Engineer who will design and deliver production-grade GenAI applications, enhancing the client journey and driving tangible business value. You’ll collaborate closely with architects, engineers, and business leaders to align solutions with Schwab’s strategic goals, and mentor junior team members—cultivating best engineering practices and a culture of continuous learning. In this pivotal role, you’ll not only advance technical standards and solve complex challenges, but also lead rapid iterations from concept to production. You will bring curiosity, creativity, and technical depth to help shape the next era of AI at Schwab, with a special emphasis on reliability, monitoring, observability, and orchestration across products. Your work will be critical in maintaining high-performance, production-grade AI solutions that support decision-making and deliver consistent value.

Requirements

8+ years of data engineering experience, including 4+ years as a hands-on senior engineer
4+ years of experience developing scalable workflows and data pipelines that support large, complex datasets
3+ years of experience designing and implementing solutions using Artificial Intelligence technologies such as natural language processing, large language models, or machine learning
2+ years of hands-on experience with containers and cloud-native platforms
Bachelor’s degree in Computer Science, Data Engineering, Mathematics, Analytics, or a related field
Applicants must be currently authorized to work in the United States on a full-time basis without employer sponsorship.

Nice To Haves

Strong foundation in data engineering across multiple layers of the technology stack
Experience building and maintaining reliable, observable, and highly available systems in production environments
Ability to solve complex problems involving distributed systems and incomplete or ambiguous data
Demonstrated commitment to engineering quality, including testing and code reliability practices
Experience mentoring engineers and supporting technical skill development through coaching and feedback
Strong written and verbal communication skills with the ability to convey complex ideas clearly
Curiosity and a continuous improvement mindset, including proactively exploring and sharing new technologies and approaches
Experience working with Python
Demonstrated business domain knowledge related to previous products or AI-driven solutions
Master’s or advanced degree in Computer Science, Data Engineering, Mathematics, Analytics, or a related field

Responsibilities

Design, build, and optimize Generative AI applications that drive measurable business impact and enhance client experiences. You’ll lead the development of robust data pipelines and prompt engineering workflows, enabling efficient model iteration and performance tuning.
Champion best practices in reliability, monitoring, and observability across AI systems, ensuring scalable and actionable reporting.
Implement a framework designed to manage unstructured data at an enterprise scale.
Collaborate with cross-functional teams to align solutions with enterprise strategy and technical standards.
Mentor and coach junior engineers, fostering strong practices and continuous learning.
Lead by example in solving complex technical challenges and driving rapid iteration from concept to deployment.
Design and maintain monitoring, alerting, and incident response frameworks to ensure system health and reliability.
Advance engineering standards, focusing on operational excellence, modular reusability, and quality across all deliverables.