MLOps Engineer

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.


Key Responsibilities


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.

Required Qualifications

• 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.

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.

Success Profile


The ideal candidate combines strong software engineering and platform expertise with a deep understanding of machine learning operations. They are passionate about reliability, scalability, automation, and enabling Data Science teams to deliver business value through production-ready machine learning solutions.