Senior AI Engineer- AI Engineering & Operations

About the role

The Senior AI Engineer plays a pivotal role in shaping the future of technology at Schwab by leading the development and deployment of Generative AI solutions. This role requires a deep understanding of AI technologies and a proven track record in delivering scalable, reliable, and high-performing AI systems.

Responsibilities

• Design, build, and optimize Generative AI applications that drive measurable business impact and enhance client experiences.
• 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.

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.

Preferred Qualifications

• 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.