About the job
AI Software Engineer - Reliability (Hybrid) - Plano, TX
Join Ziosk, where our passion lies in enhancing the dining experience for guests and empowering restaurants to thrive!
Have you ever noticed a tablet at your table for payment? We were the pioneers in the pay-at-the-table concept and are on a mission to revolutionize the restaurant industry. Our ongoing success is driven by our adaptability to meet the needs of our esteemed clients, including Olive Garden, Texas Roadhouse, and Chili’s, by delivering exceptional experiences that encourage guest loyalty. Our extensive range of solutions encompasses hardware, software, cloud-based services, and AI-driven innovations, all aimed at maximizing guest satisfaction and profitability.
Our secret ingredient? Our talented team! Each day, they are crafting innovative solutions, establishing Ziosk as the premier pay-at-the-table provider in the market.
Are you ready to join our team? Ziosk is seeking a seasoned AI Software Engineer - Reliability to enhance our Engineering team. This role merges advanced technical debugging skills with AI-driven tools and frameworks to expedite issue diagnosis, minimize resolution times, and bolster system reliability across the Ziosk platform. You will utilize AI agents, intelligent log analysis, automated root cause workflows, and code-assist tools to decrease Mean Time to Recovery (MTTR), optimize defect triage speed, eliminate repetitive manual debugging, and proactively identify systemic reliability threats. This position integrates software engineering, production operations, and applied AI automation.
Key Responsibilities:
- Diagnose and resolve intricate production issues across Android (C++, Kotlin, React Native) and . NET systems utilizing logs, telemetry, and AI-assisted analysis.
- Design and enhance AI-enabled log analysis and incident triage workflows to expedite root cause identification.
- Leverage AI-assisted development tools to efficiently generate, test, and validate code fixes.
- Identify repetitive break/fix patterns and develop AI-driven automation to lessen manual debugging efforts.
- Integrate AI-supported diagnostics and automation into CI/CD pipelines and defect tracking systems.
- Implement durable fixes that mitigate repeat incidents and enhance overall system reliability.
- Build and refine retrieval-based (RAG) workflows to surface historical incident context and detect recurring patterns.
- Document resolutions and automation improvements to broaden AI-assisted resolution coverage and reduce MTTR.

