AI-Native Technical Support Engineer

Description

Who are we?

Trucker Path is a leading mobile platform built specifically for professional truck drivers and fleets. It offers truck-safe GPS navigation, real-time parking availability, weigh station status, fuel prices, and route planning tools. The platform helps drivers operate more efficiently, reduce costs, and stay compliant on the road. NavPro enhances large fleet performance with advanced tracking and compliance tools, providing greater visibility and control across operations.

Role purpose 

Own NavPro’s AI-native front-line technical support and escalation quality for fleet customers: resolve issues quickly, improve customer experience, and create a tight feedback loop from real-world incidents ? root cause ? product/engineering action ? verified improvement. 

Key responsibilities 

Operate AI-native support workflows:

· Use AI to draft customer updates, internal incident summaries, and escalation notes that are accurate and action-oriented. 

· Use AI to propose likely causes, next-best tests, and targeted data requests, then validate with evidence. 

· Build lightweight automations (macros, scripts, dashboards) to reduce repetitive work and improve resolution speed. 

· Maintain high signal-to-noise documentation (what happened, why, what we learned, what we changed). 

Handle fleet customer technical issues:

· Diagnose problems, reproduce bugs, and resolve issues directly when possible. 

· Guide users through troubleshooting steps with clear, timely updates. 

Escalate effectively to Tier 2 engineering:

· Escalate unresolved issues with complete documentation. 

· Include repro steps, logs, screenshots, and customer context to enable fast resolution. 

Convert recurring issues into product and technical solutions (AI-native):

· Use AI to summarize tickets, cluster themes, and surface the highest-impact patterns. 

· Run structured investigations (repro steps, logs, request traces, data checks, environment diffs). 

· Write crisp problem statements, root-cause hypotheses, and proposed fixes or workarounds. 

· Define acceptance criteria, rollout plans, and success metrics.

· Partner with Product and Engineering to drive items onto the roadmap, ship fixes, and validate post-release. 

Build and maintain support knowledge and tooling: 

· Maintain a knowledge base of common issues and solutions. 

· Create internal runbooks and troubleshooting checklists. 

· Identify automation opportunities for deflection and faster resolution. 

Track and improve support performance:

· Track response time, resolution time, and escalation rate. 

· Identify top drivers of volume and drive measurable improvements.

Requirements

· 1–3+ years in technical support, customer support engineering, implementation support, or similar technical customer-facing roles. 

· Strong troubleshooting skills across logs, error messages, networking basics, APIs, system configuration, and debugging mobile apps and web portals. 

· Sufficient backend engineering literacy (Java/Spring Boot, AWS cloud services) to critically evaluate AI-generated technical analysis, validate root-cause hypotheses, and make informed escalation decisions

· AI-forward working style: uses AI assistants daily for synthesis, investigation, writing, and iteration. 

· Excellent customer communication skills and ability to explain technical concepts clearly. 

· Excellent written English for technical documentation and customer correspondence. 

· Preferred: B2B enterprise support experience, familiarity with fleet or logistics software, and experience supporting production mobile applications and web portals. 

What success looks like (first 90 days):

· Faster first response and time-to-resolution for top fleet issue categories. 

· Higher-quality escalations (complete repros, logs, and clear hypotheses), reducing engineering back-and-forth. 

· A living knowledge base and runbooks that deflect repeat issues and speed up onboarding. 

· A steady pipeline of well-formed product feedback tied to measurable impact (volume, severity, churn risk).