Machine Learning / AI Engineer, CA / Hybrid, 18+Months

Dice is the leading career destination for tech experts at every stage of their careers. Our client, Amtex Enterprises, is seeking the following. Apply via Dice today!

Machine Learning/AI Engineer III

Duration: 18+ Months

Rate: Open

Location: Onsite in San Jose, CA (3 days onsite and 2 days remote)- (In-office Tuesday, Wednesday, Thursday)

Top 3 Must-Have Hard Skills

• BigQuery, Python
• Understanding of production system architecture and offline data workflows
• Machine Learning experience

What You’ll Do

• Redesign and optimize PayPal’s MLOps and decision platform for fraud detection.
• Architect large-scale big-data infrastructure to enable cutting-edge machine learning models for real-time fraud prevention.
• Collaborate with data scientists and platform engineers to automate workflows.
• Deliver solutions ensuring compliance, security, and maintainability across the fraud detection ecosystem.
• Work with high-dimensional datasets using tools like Python, PySpark, and BigQuery to develop robust fraud signal detection workflows.
• Standardize rules and decision processes while supporting dynamic rule updates and analytics within the fraud detection platform.
• Collaborate across multidisciplinary teams in engineering, product development, and data science to scale solutions globally.

Story Behind the Need – Team & Key Projects

• Team: You will work closely with Core Automation team tech leads and managers.
• Key Projects: Rebuild PayPal’s next-generation risk platform (NGRP) with engineering teams; be part of the pioneering team launching the NGRP.
• Reason for Posting: Build a next-generation risk platform supporting all enterprise business functions under a tight timeline for decision engine adoption.

Work & Collaboration Structure

• Tasks managed via Jira board, sprint planning, and grooming cycles.
• Regular team stand-ups at least twice a week (daily if needed for blockers).
• Onboarding involves more interaction with Engineering, Product, and US Risk core teams; post-onboarding, interactions will be ~50/50.
• Tasks tracked primarily through Jira; code maintained in a centralized GitHub repository.
• Documentation and updates maintained on wiki pages or SharePoint/Shared Drives.