Machine Learning Ops Data Engineer

Overview In this role you lead the end-to-end productionization of AI/ML initiatives on GCP, owning architecture, deployment, and operations. You collaborate with data scientists, MLOps, and engineers to deliver secure, scalable solutions that align with Schwab’s mission to transform finance. You will drive high-impact projects from prototype to production while ensuring reliability, security, and cost efficiency. This on-site position offers a hands-on leadership path within a collaborative, problem-solving culture. ResponsibilitiesDesign and build production-ready AI/ML use cases on GCP, with a focus on security-related outcomesLead end-to-end deployment from prototype to production with clear quality gatesDocument and resolve technical debts and aging componentsImplement coding standards, testing strategies, data quality checks, alerting mechanisms, and runbooksEnsure platform reliability, security, and cost efficiencyMentor MLOps and data engineers while staying hands-on in delivery Key requirements8+ years in data/software engineering with 2+ years in technical leadershipProven track record delivering production-grade AI/ML on GCP or other cloud providersExperience building and operating scalable batch/streaming pipelinesExperience leading design reviews and mentoring engineersSupport of critical systems in productionExperience partnering with data scientists/MLE/Ops teams to deliver business outcomesExpert-level Google Cloud experience with AI/ML services (BigQuery, Vertex AI, GCS, Dataflow, Pub/Sub, Cloud Run/GKE, Composer/Airflow, IAM, Cloud Monitoring/Logging)Expert Python for production-grade data/backend engineeringStrong SQL and data modeling for analytics, scalability, and operational workloadsStrong CI/CD and containerization (Docker, Git workflows, automated testing, release pipelines)Solid cloud security and governance (IAM, secrets, least privilege, auditability)Strong observability and reliability engineering (monitoring, alerting, incident response, SLAs/SLOs)Understanding of AI/ML lifecycle (training/serving integration, model versioning, pipeline monitoring)leadershipmentoringcross-functional collaborationGCP services for AI/ML: Vertex AI, BigQuery, Dataflow, Pub/Sub, Cloud Run/GKE, Composer/Airflow, IAM, Cloud Monitoring/LoggingPython production engineeringSQL and data modeling