Machine Learning Ops Data Engineer
Charles Schwab6 days ago
Responsibilities
- Design and build production-ready AI/ML-powered, security-related use cases on Google Cloud Platform.
- Lead end-to-end deployment of AI/ML systems from prototype to production with clear quality gates.
- Build and operate scalable batch and streaming data pipelines.
- Implement coding standards, test strategies, data quality checks, alerting mechanisms, and operational runbooks.
- Ensure platform reliability, security, governance, observability, and cost efficiency.
- Understand, document, and lead resolution of technical debt.
- Support critical production systems and model versioning, training/serving integration, and pipeline monitoring.
- Lead design reviews, enforce engineering standards, and mentor MLOps and data engineers while remaining hands-on.
- Partner with data scientists, MLE, and Ops teams to deliver business outcomes.
Requirements
- 8+ years of experience in data or software engineering.
- 2+ years of technical leadership experience.
- Expert Google Cloud experience, especially with AI/ML services and production workloads.
- Expert Python skills for production-grade data and backend engineering.
- Strong SQL and data modeling skills for analytics, scalability, and operational workloads.
- Strong containerization, Git workflow, automated testing, and release pipeline skills.
- Solid cloud security and governance knowledge, including IAM, secrets, least privilege, and auditability.
- Strong observability and reliability engineering skills, including monitoring, alerting, incident response, and SLAs/SLOs.
- Fundamental understanding of the AI/ML lifecycle and model development required to productionize AI/ML systems.
- Proven record delivering production AI/ML use cases on GCP or other cloud providers.
- Experience building and operating scalable batch and streaming pipelines.
- Experience leading design reviews, enforcing engineering standards, mentoring data engineers, and supporting critical production systems.
- Experience partnering with data scientists and MLE/Ops teams.
Benefits
- The role is intended to be performed on site in the specified location(s), with an emphasis on in-office collaboration.
- Employees are empowered to make an impact and encouraged to challenge the status quo in the finance industry.
Tech Stack
Categories
Data EngineeringML Engineering
About Charles Schwab
Charles Schwab is a different kind of investment services firm – one that strives to disrupt the status quo of the traditional Wall Street approach on behalf of our clients. We believe today, as we did on Day 1, that when you find ways to improve the investing experience for your clients, then business results will follow. Follow our company culture at #SchwabLife and see how we give back at #Schwab4Good. Support hours: 7 a.m.–7 p.m. CT or 24/7 at schwab.com/contact-us. Social Media Disclosures: https://www.aboutschwab.com/social-media (#0424-TM8W)