AI/ML Ops and Data Engineer
About The Position
At Schwab, this role is a hands-on technical lead responsible for taking AI/ML projects from development to production on Google Cloud Platform (GCP). It owns architecture, implementation, deployment, and operations, with an emphasis on production-ready, security-related use cases. The selected candidate is expected to work on site in the specified location(s), reflecting the company’s emphasis on in-office collaboration. The role leads end-to-end delivery from prototype to production, establishes quality gates and engineering standards, addresses technical debt, and supports reliability, security, and cost efficiency. It also mentors MLOps and data engineers while contributing directly to code and delivery, and partners with data scientists, machine-learning engineers, and operations teams to achieve business outcomes.
Requirements
- 8+ years in data/software engineering, including 2+ years in technical leadership.
- Expert-level Google Cloud experience, especially services used for AI/ML, such as BigQuery, Vertex AI, GCS, Dataflow, Pub/Sub, Cloud Run/GKE, Composer/Airflow, IAM, and Cloud Monitoring/Logging.
- Expert Python skills for production-grade data and backend engineering.
- Strong SQL and data modeling skills for analytics, scalability, and operational workloads.
- Strong CI/CD and containerization skills, including Docker, Git workflows, automated testing, and release pipelines.
- Solid cloud security and governance practices, 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 needed to productionize AI/ML systems, including training/serving integration, model versioning, and pipeline monitoring support.
- Proven track record delivering production-grade AI/ML use cases on GCP or other cloud providers.
- Experience building and operating scalable batch/streaming pipelines.
- Demonstrated experience supporting critical systems in production.
- Experience partnering with data scientists/MLE/Ops teams to deliver business outcomes.
Responsibilities
- 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 debt.
- Implement coding standards, test strategy, data quality checks, alerting mechanisms, and operational runbooks.
- Ensure platform reliability, security, and cost efficiency.
- Mentor MLOps and data engineers while remaining hands-on in code and delivery.
- Lead design reviews, enforce engineering standards, and mentor data engineers.
- Build and operate scalable batch and streaming pipelines.
- Support critical systems in production.
- Partner with data scientists, MLE/Ops teams to deliver business outcomes.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
