Python and ml frameworks like tensorflow or pytorch
Experience with airflow, kubeflow, docker, kubernetes
This role is part of the Machine Learning Engineering teams at UPS Technology, building next-generation intelligent systems that power the Smart Logistics Network
Job Summary
This role is part of the Machine Learning Engineering teams at UPS Technology, building next-generation intelligent systems that power the Smart Logistics Network.
The engineer will operationalize ML models for marketing, including Propensity to Buy, Churn Prediction, and Customer Lifetime Value, ensuring low-latency inference and automated pipelines.
Candidates must bridge Data Science and Engineering by transforming experimental models into scalable, monitored, and business-ready solutions within the Global Customer Platform.
Matching Summary
This role is part of the Machine Learning Engineering teams at UPS Technology, building next-generation intelligent systems that power the Smart Logistics Network.
Skills & Requirements
Must-have
5-10+ years in data engineering or ML engineering
Python and ML frameworks like TensorFlow or PyTorch
Experience with Airflow, Kubeflow, Docker, Kubernetes
Cloud platform expertise (Azure, AWS, or GCP)
Feature store infrastructure and model lifecycle management
Nice-to-have
Experience in marketing analytics or customer data platforms
Familiarity with CDP integrations and real-time personalization
Understanding of customer segmentation and campaign workflows
Experience implementing ML governance and compliance standards
Key Requirements
5–10+ years experience in data engineering, ML engineering, or MLOps
Strong proficiency in Python and ML frameworks
Proven track record deploying ML models into production environments