Machine Learning Ops Data Engineer

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 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).  

Hands-on technical lead responsible for taking AI/ML projects from development to production in Google Cloud Platform (GCP). This role owns architecture, implementation, deployment, and operations.

• Expert-level Google Cloud experience, especially services used for AI/ML use cases (e.g., BigQuery, Vertex AI, GCS, Dataflow, Pub/Sub, Cloud Run/GKE, Composer/Airflow, IAM, Cloud Monitoring/Logging)
• Expert Python for production-grade data and backend engineering
• Strong SQL and data modeling for analytics, scalability, and operational workloads
• Strong CI/CD and containerization skills (Docker, Git workflows, automated testing, release pipelines)
• Solid cloud security and governance practices (IAM, secrets, least privilege, auditability)
• Strong observability and reliability engineering skills (monitoring, alerting, incident response, SLAs/SLOs)
• Fundamental understanding of AI/ML lifecycle/model development needed to productionize AI/ML systems (training/serving integration, model versioning, pipeline monitoring support)

• 8+ years in data/software engineering, including 2+ years in technical leadership
• Proven track record delivering production grade AI/ML use cases on GCP or other cloud providers
• Experience building and operating scalable batch/streaming pipelines
• Experience leading design reviews, enforcing engineering standards, and mentoring data engineers
• Demonstrated support of critical systems in production
• Experience partnering with data scientists/MLE/Ops teams to deliver business outcomes

• →Design and build production-ready AI/ML powered, security related use cases on GCP
• →Lead end-to-end deployment from prototype to production with clear quality gates
• →Understand, document, and lead the resolution of technical debts
• →Implement coding standards, test strategy, data quality checks, alerting mechanisms, and operational runbooks
• →Ensure platform reliability, security, and cost efficiency
• →Mentor the MLOps and data engineers while remaining hands-on in code and delivery