ML Ops Engineer
Core
Architect storage and compute, harden training/inference pipelines, and ensure ML code, data workflows, and services are reliable, reproducible, observable, and cost-efficient.
Role type
Senior hands-on ML Ops Engineer
Builds
Production-ready ML systems for performance marketing and customer acquisition
Domain
Performance marketing / Customer acquisition / Cloud infrastructure
Deliverable
production ML models
Required skills
Python, CI/CD, AWS, Docker, Kubernetes, Terraform, MLflow, PySpark, Kafka, Model serving, Feature stores, Observability
Preferred skills
DataBricks, Glue, Dask, 3rd party data integration, Model governance
Technologies
AWS, DataBricks, Docker, Kubernetes, Terraform, CloudFormation, MLflow, SageMaker, PySpark, Glue, Dask, Kafka
Responsibilities
Productionize training and inference (batch/real-time), establish CI/CD for models, centralize feature generation, implement monitoring for data quality and drift, refactor research code into reusable components, drive technical vision for ML platform
Seniority
Senior, hands-on IC
