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About the Role
As a MLOps Engineer at Global, you’ll build the operational infrastructure that brings AI and ML models into production. You’ll own the platforms, pipelines and processes that let our Data Science teams deploy, monitor, retrain and govern models reliably at scale—from the ground up.
Responsibilities
- Build automated pipelines for model training, validation and deployment, plus model registries, feature stores and inference services, with self-serve tooling for Data Science teams.
- Implement monitoring, alerting and automated recovery for ML workloads—covering latency, data quality and drift—and own rollback, rollout and incident response.
- Establish controls for model lineage, reproducibility and audit trails, and introduce ML-specific CI/CD, testing and release automation.
- Partner with Data Science, Data Engineering and Product, and mentor junior engineers to raise operational standards.
Requirements
- Operationalized ML models in production, owning deployment, monitoring and lifecycle management.
- Production-quality, testable Python.
- Deep AWS knowledge (SageMaker, Lambda, ECS/EKS, Step Functions); Snowflake a plus.
- Experience with experiment tracking and registries, workflow orchestration, model serving and feature stores.
- Experience with ML-specific CI/CD, Terraform, Docker and test automation.
- Ability to translate between Data Science and Engineering and explain trade-offs to any audience.
Skills
- Python
- AWS
- SageMaker
- Lambda
- ECS/EKS
- Step Functions
- Snowflake
- Terraform
- Docker
Location
- Global
Work Type
- Full-time
Experience Level
- Mid-level
About the Company
- Global:IQ is the team building our new intelligence platform, turning first-party and partner data into smarter, data-led media plans across Global’s audio and Outdoor inventory.