MLOps Data Engineer – Production ML on GCP (On-Site)

Charles Schwab; Phoenix (AZ); On-site; USD 140,000 - 200,000; Full time

Job summary

Charles Schwab is seeking a hands-on technical lead to drive AI/ML projects from development to production on Google Cloud Platform. You will own architecture, implementation, deployment, and operations, collaborating with data scientists and Ops teams in a security-conscious environment.

The role emphasizes end-to-end deployment, coding standards, and reliability, with a strong focus on security, observability, and cost efficiency while mentoring MLOps and data engineers.

Qualifications

• 8+ years in data/software engineering with 2+ years in technical leadership.
• Proven production-grade AI/ML deliveries on GCP or other clouds.
• Experience building and operating scalable batch/streaming pipelines.
• Experience leading design reviews, enforcing standards, mentoring data engineers.
• Demonstrated support of critical systems in production.
• Experience partnering with data scientists/Ops/MLE teams to deliver 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 debts.
• Implement coding standards, test strategy, data quality checks, alerting mechanisms, and runbooks.
• Ensure platform reliability, security, and cost efficiency.
• Mentor the MLOps and data engineers while remaining hands-on in code and delivery.

Skills

GCP expertise

Python programming

SQL & data modeling

CI/CD & containerization

Cloud security & governance

Observability & SRE

AI/ML lifecycle knowledge

Tools

BigQuery

Vertex AI

GCS

Dataflow

Pub/Sub

Cloud Run

GKE

Composer/Airflow

IAM

Cloud Monitoring/Logging

Docker

Git

Job description

Charles Schwab is seeking a hands-on technical lead to drive AI/ML projects from development to production on Google Cloud Platform. You will own architecture, implementation, deployment, and operations, collaborating with data scientists and Ops teams in a security-conscious environment.

The role emphasizes end-to-end deployment, coding standards, and reliability, with a strong focus on security, observability, and cost efficiency while mentoring MLOps and data engineers.