AI Support Engineer

Private Advertiser; Philippines; On-site; PHP 600,000 - 900,000; Full time

Job summary

Private Advertiser is seeking an AI Support Engineer to troubleshoot complex issues on an AI Platform, bridging customers and engineering teams—reproducing bugs, providing workarounds, writing code fixes, and communicating clearly via tickets and calls.

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

Qualifications

• 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.

Responsibilities

• Customer Troubleshooting: Solve complex user issues via tickets, email, and live calls during standard hours.
• Incident & On-Call Response: Join on-call rotation and act as an Incident Communicator during SaaS production incidents.
• Engineering Collaboration: Partner with developers, product managers, and data scientists to reproduce edge-case bugs and deliver fixes.
• Workarounds & Knowledge Base: Create temporary workarounds and write knowledge base articles to prevent repeats.
• AI Automation Projects: Help build internal models and scripts to automate support workflows and improve resolution times.

Skills

Customer support

Linux administration

Python scripting

ML fundamentals

Communication skills

Incident management

Education

Bachelor's degree in CS / Data Science / Engineering

Tools

Salesforce ServiceCloud

Kubernetes

Docker

AWS

Azure

GCP

SQL/NoSQL

Job description

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.