AI Technical Support Engineer

Role Overview

We are seeking an client-facing AI Support Specialist to resolve high-tier technical challenges across our AI platform. In this position, you will serve as the core link between enterprise clients and internal product engineering—investigating issues, executing code-level fixes, and guiding users toward quick resolutions through tickets and live calls.

Key Responsibilities

• Client Issue Resolution: Diagnose and resolve complex technical inquiries submitted via Salesforce ServiceCloud, email, and live video sessions during UTC coverage hours.
• Incident Management: Participate in the weekend Sev-1 on-call schedule and manage client communications during critical SaaS service disruptions.
• Engineering Escalations: Work side-by-side with data scientists, product managers, and developers to isolate edge cases and implement lasting bug fixes.
• Solutions & Documentation: Develop technical workarounds for unique edge cases and author knowledge-base guides to streamline future troubleshooting.
• Support Automation: Contribute to internal tooling and automation scripts aimed at optimizing support workflows and response speeds.

Requirements

• Education: Bachelor’s degree in Computer Science, Data Science, Engineering, or relevant technical experience.
• Enterprise Support: 2+ years assisting enterprise clients on complex software platforms, including practical experience with ticketing systems (Salesforce ServiceCloud preferred).
• Systems & OS: Proven track record in Linux administration, log analysis, and enterprise network diagnostics.
• Programming: Practical ability to analyze code bases and write Python scripts for workflow automation and issue replication.
• AI/ML Concepts: Functional knowledge of the end-to-end Machine Learning lifecycle and modern AI frameworks.
• Communication: Stellar written and spoken English communication skills for client-facing and internal coordination.

Preferred Qualifications

• Background supporting enterprise AI, Data Analytics, or ML platforms.
• Familiarity with containerized applications (Docker, Kubernetes).
• Exposure to cloud infrastructure (AWS, Azure, GCP) and database management (SQL/NoSQL).
• Previous experience managing critical incidents for SaaS applications.