At Schwab, you’re empowered to make an impact on your career. Here, innovative thought meets creative problem solving, helping us challenge the status quo and transform the finance industry together. We succeed as One Schwab—collaborating with trust, integrity, and a shared commitment to doing the right thing for our clients and each other.
We believe in the importance of in-office collaboration and fully intend for the selected candidate for this role to work on site in the specified location(s).
We are looking for a Senior AI/ML Engineer (individual contributor) who will join our Global Data and Analytics team to build and evolve a large scale data intelligence platform on Google Cloud Platform (GCP). In this role, you will build and scale fraud detection and decisioning capabilities across real-time and batch data pipelines. This role is ideal for someone who can design production-grade ML systems end-to-end: from data ingestion and feature engineering to model deployment, monitoring, and business decision integration.
You’ll work at the intersection of data architecture and AI, engineering resilient pipelines that enable advanced analytics and machine learning models at scale. This is a hands on, end to end engineering role where your work directly supports teams protecting clients and strengthening trust across the firm.
Key Responsibilities
• Design and build ML pipelines that combine streaming events and batch processing
• Develop, deploy, and optimize models for risk scoring in user login, transactions and payment journeys
• Create robust feature engineering pipelines and reusable feature definitions for both online and offline use
• Integrate model outputs into operational decision systems
• Implement MLOps best practices in standardizing the entire machine learning lifecycle
• Improve data quality and reliability through schema governance, deduplication, lineage, and data validation checks
• Partner with risk analysts and product stakeholders to convert business rules and feedback into model improvements
• Contribute to analytics, dashboards, and LLM-assisted tooling that helps teams understand fraud patterns and model behavior
What you have
Required Qualifications
• 8+ years of professional experience in software, data, or machine learning engineering
• 3-4+ years of hands-on AI/ML experience building and deploying ML models in production
• Advanced Python and SQL skills with deep BigQuery experience in large-scale data processing
• Proven experience with both real-time and batch pipeline design
• Demonstrated ability to own data preparation and quality
• Strong understanding of feature engineering
• Experience with cloud ML and data platforms for model training/deployment, data warehouse and orchestration
• Experience integrating ML models into low-latency production systems for real time insights and decisioning
Preferred Qualifications
• Master’s or advanced degree in computer science or a related field
• Experience with event-driven architectures and streaming platforms such as Kafka or Pub/Sub
• Experience applying GenAI/LLM capabilities to analytics workflows
• Experience applying advanced analytical techniques such as graph analytics, anomaly detection, entity linking for pattern detection
• Hands-on experience with GCP ML services
• Experience working in regulated environments with audit, compliance, and model governance requirements
• Prior experience in fraud, risk, or financial crime analytics