MLOPS Engineer - Remote

Duration: : Long Term Contract

Role: : 100% Remote

Must haves: PYTHONGCPMLOPSEnterprise level Experience Individual Contributor

Overview: 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.

Productionization & Deployment: 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.

Platform Engineering: 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.

Operational Excellence: 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.

Data & Pipeline Engineering: 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.

Preferred Qualifications: 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.