Machine Learning/AI Engineer III

Overview

Hybrid
Depends on Experience
Contract - W2
Contract - 18 month(s)
25% Travel

Skills

python
Machine Learning
MLOps
BigQuery
production system architecture
offline data workflows
risk platform

Job Details

Role: Machine Learning/AI Engineer
Location: Onsite in San Jose, CA (3 days onsite and 2 days remote)- (In-office Tuesday, Wednesday, Thursday)
Duration: 18+ Months
 
Job Description:
 
Top 3 Must-Have Hard Skills
  1. BigQuery, Python
  2. Understanding of production system architecture and offline data workflows
  3. Machine Learning experience
 
What You’ll Do
  • Redesign and optimize Client'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 Client'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.
 
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