MLOps Engineer

Saxon Global•Sunnyvale, CA

About The Position

The Client is seeking experienced MLOps Engineers to support the productionization, scalability, and operational excellence of enterprise advertising technology solutions. These engineers will play a critical role in transforming proven machine learning capabilities into resilient, cost-effective, multi-tenant production platforms. This position focuses on deploying, monitoring, optimizing, and maintaining machine learning systems supporting search, recommendation, forecasting, advertising optimization, and advanced analytics use cases.

Requirements

5+ years of experience in MLOps, Machine Learning Engineering, Data Engineering, or Platform Engineering.
Strong Python development experience.
Experience deploying machine learning solutions in cloud environments.
Hands-on expertise with: GCP, Databricks, Airflow, CI/CD pipelines, Infrastructure automation
Experience supporting production machine learning systems.

Nice To Haves

Experience supporting search, recommendation, ranking, or advertising platforms.
Knowledge of model monitoring and observability frameworks.
Experience with cloud cost optimization and performance tuning.
Experience supporting multi-tenant platforms.
Familiarity with modern MLOps frameworks and deployment patterns.

Responsibilities

Deploy machine learning models into scalable production environments.
Build and maintain CI/CD pipelines for machine learning workloads.
Partner closely with Data Scientists to operationalize new models and capabilities.
Implement model serving and deployment strategies across multiple business units.
Design and support scalable multi-tenant machine learning infrastructure.
Improve platform reliability, performance, and observability.
Develop reusable deployment frameworks and infrastructure templates.
Ensure platform consistency across multiple regions and business domains.
Monitor model health, performance, data quality, and system reliability.
Establish alerting, logging, and automated remediation processes.
Optimize infrastructure utilization and cloud spend.
Troubleshoot production incidents and drive root-cause resolution.
Build and optimize batch and real-time ML pipelines.
Improve workflow orchestration and data movement processes.
Support feature engineering pipelines and model retraining frameworks.
Ensure reliability and scalability of production data infrastructure.