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Machine Learning Ops Data Engineer

Southlake, TX, US • Posted 1 day ago • Updated 8 hours ago

Full Time

On-site

USD $102,000.00 - 185,000.00 per year

Fitment

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Job Details

Skills

Creative Problem Solving
Finance
Collaboration
Vertex
GCS
Data Flow
Python
SQL
Data Modeling
Analytics
Scalability
Continuous Integration
Continuous Delivery
Docker
Git
Workflow
Automated Testing
Cloud Security
Reliability Engineering
Incident Management
Training
Software Engineering
IT Management
Cloud Computing
Streaming
Design Review
Artificial Intelligence
Machine Learning (ML)
Use Cases
Google Cloud
Google Cloud Platform
Test Strategy
Data Quality
Mentorship
Machine Learning Operations (ML Ops)

Creative Problem Solving
Finance
Collaboration
Vertex
GCS
Data Flow
Python
SQL
Data Modeling
Analytics
Scalability
Continuous Integration
Continuous Delivery
Docker
Git
Workflow
Automated Testing
Cloud Security
Reliability Engineering
Incident Management
Training
Software Engineering
IT Management
Cloud Computing
Streaming
Design Review
Artificial Intelligence
Machine Learning (ML)
Use Cases
Google Cloud
Google Cloud Platform
Test Strategy
Data Quality
Mentorship
Machine Learning Operations (ML Ops)

Summary

Your Opportunity

At 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 (Google Cloud Platform). This role owns architecture, implementation, deployment, and operations.

Required Skills

Expert-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 engineering
Strong SQL and data modeling for analytics, scalability, and operational workloads
Strong 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 have

Required Work Experience

8+ years in data/software engineering, including 2+ years in technical leadership
Proven track record delivering production grade AI/ML use cases on Google Cloud Platform or other cloud providers
Experience building and operating scalable batch/streaming pipelines
Experience leading design reviews, enforcing engineering standards, and mentoring data engineers
Demonstrated support of critical systems in production
Experience partnering with data scientists/MLE/Ops teams to deliver business outcomes

Core Responsibilities

Design and build production-ready AI/ML powered, security related use cases on Google Cloud Platform
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 operational runbooks
Ensure platform reliability, security, and cost efficiency
Mentor the MLOps and data engineers while remaining hands-on in code and delivery

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Dice Id: 90989465
Position Id: 65cb6bcebd32aabacaf11937674d16e9
Posted 1 day ago

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