MLOPS Engineer - Remote

Position: MLOPS Engineer • Duration: Long Term Contract • Role: 100% Remote

About the role

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.

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.

Requirements

Required:

• 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:

• 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.