AI Support Engineer

Role Overview

We are looking for a customer-facing AI Support Engineer to troubleshoot complex technical issues for users on an AI Platform. You will bridge the gap between our customers and our engineering teams—reproducing bugs, providing workarounds, writing code fixes, and communicating clearly over tickets and calls.

If you have a background in Linux administration, basic Python scripting, and a solid grasp of the Machine Learning lifecycle, this role is for you.

What You’ll Do

• Customer Troubleshooting: Solve complex user issues via Salesforce ServiceCloud tickets, email, and live Zoom/Webex calls during standard UTC hours.
• Incident & On-Call Response: Join the weekend Sev-1 on-call rotation and act as an Incident Communicator during SaaS production incidents (DataRobot MTS).
• Engineering Collaboration: Partner with developers, product managers, and data scientists to reproduce edge-case bugs and deliver permanent fixes.
• Workarounds & Knowledge Base: Create temporary workarounds when standard procedures fail, and write internal/external knowledge base articles to prevent repeat issues.
• AI Automation Projects: Help build internal models and scripts to automate support workflows and improve resolution times.

What We’re Looking For

Must-Haves:

• Education: Bachelor’s degree in Computer Science, Data Science, Statistics, Engineering, or equivalent experience.
• Support Experience: 2+ years supporting enterprise tech/data customers, with hands-on experience using Salesforce ServiceCloud.
• Linux & Systems: Strong experience administering Linux environments, reading/monitoring logs, and troubleshooting network issues.
• Python Skills: Ability to read code and write basic Python scripts for automation and debugging.
• Machine Learning Fundamentals: Solid understanding of the ML model training lifecycle and general AI tools.
• Communication: Exceptional written and verbal English skills to translate technical concepts to users and engineering teams alike.

Nice-to-Haves:

• Direct experience supporting Machine Learning, AI, or Data Science platforms.
• Hands-on experience with Kubernetes and Docker deployments.
• Experience with cloud platforms (AWS, Azure, GCP) and databases (SQL / NoSQL).
• Prior incident management (IR) experience on SaaS platforms.