AI/ML Ops and Data Engineer

Your OpportunityAt 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.Required SkillsExpert-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 engineeringStrong SQL and data modeling for analytics, scalability, and operational workloadsStrong 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)What You HaveRequired Work Experience8+ years in data/software engineering, including 2+ years in technical leadershipProven track record delivering production grade AI/ML use cases on GCP or other cloud providersExperience building and operating scalable batch/streaming pipelinesExperience leading design reviews, enforcing engineering standards, and mentoring data engineersDemonstrated support of critical systems in productionExperience partnering with data scientists/MLE/Ops teams to deliver business outcomesCore ResponsibilitiesDesign and build production-ready AI/ML powered, security related use cases on GCPLead end-to-end deployment from prototype to production with clear quality gatesUnderstand, document, and lead the resolution of technical debtsImplement coding standards, test strategy, data quality checks, alerting mechanisms, and operational runbooksEnsure platform reliability, security, and cost efficiencyMentor the MLOps and data engineers while remaining hands-on in code and delivery