Customer Tech Care Lead Customer Care Department - Remote

StellarTech is building a Customer Tech Care unit - a specialised engineering squad embedded within Customer Care, whose sole purpose is to technically resolve every user issue that cannot be solved by a standard support agent. As the Lead, you will be the technical authority and organisational backbone: triaging escalated tickets, directing your team of automation engineers, coordinating with product and infrastructure engineering, and personally architecting or approving every solution shipped.

This is not a traditional support lead role. You will spend the majority of your time in systems - querying databases, reproducing bugs in staging, reading logs, designing automation flows, and evaluating LLM agents. You are the bridge between what support teams observe and what engineering teams build.

What You’ll Do:

• Team Leadership & Unit Operations

• Own day-to-day operation: capacity planning, sprint structure, task distribution, and quality standards.

• Mentor and grow automation engineers; define code and architecture review processes.

• Establish and track unit KPIs: ticket deflection rate, automation coverage ratio, recurrence prevention.

• Report unit performance to management; propose headcount and tooling investments backed by data.

• Escalation Triage & Technical Investigation

• Serve as the primary technical intake point for all escalated issues: reproduce problems, isolate root causes, scope resolution paths.

• Access production/staging systems, internal dashboards, and databases (SQL/NoSQL) to investigate issues directly.

• Analyse logs, event streams, payment records, subscription states, and user journey data.

• Own the diagnostic protocol and ensure team members follow structured investigation runbooks.

• Automation Solution Architecture

• Design and oversee automation solutions that resolve recurring issue categories without human intervention.

• Architect multi-step pipelines spanning Zendesk triggers, internal API calls, and database state changes.

• Define the solution taxonomy (macro vs. API vs. LLM agent vs. DB script) and enforce it across the team.

• Approve all automation flows before production deployment; enforce rollback and monitoring standards.

• LLM & AI Agent Delivery

• Lead design and deployment of LLM-based agents capable of diagnosing and resolving issues autonomously.

• Define agent architecture for context injection: user profile, subscription state, events, ticket history from DB.