Machine Learning Ops Data Engineer
On-sitePhoenix, Arizona, United States or Austin, Texas, United States
Job Summary
Design and build production-ready AI/ML powered, security related use cases on GCP, leading end-to-end deployment from prototype to production with clear quality gates. Implement coding standards, test strategy, data quality checks, alerting mechanisms, and operational runbooks to ensure platform reliability, security, and cost efficiency. Mentor the MLOps and data engineers while remaining hands-on in code and delivery, understanding and leading the resolution of technical debts. Requires 8+ years in data/software engineering with 2+ years in technical leadership and a proven track record delivering production grade AI/ML use cases on GCP. Full-time on-site role in the specified location.
Required Qualifications
- 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)
- 8+ years in data/software engineering, including 2+ years in technical leadership
- Proven track record delivering production grade AI/ML use cases on GCP 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
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