Backend Engineer (Application Support)

Backbase; Castilla-La Mancha; Presencial; EUR 45.000 - 65.000; Jornada completa

Descripción de la vacante

Backbase is looking for an AI Applied Engineer in Spain, Castilla-La Mancha, to enhance the stability of their digital banking systems. You'll help address escalations, automate processes, and optimize workflows using AI tools, while participating in an on-call rotation.

The ideal candidate has 3 to 5 years of backend development experience, with a strong foundation in Java, Spring Boot, and Kubernetes. This role is crucial for maintaining high system performance and reliability.

Formación

• 3 to 5 years of experience in backend software engineering or application support.
• Strong hands-on experience with Java and Spring Boot.
• Experience in microservices architectures and Kubernetes.

Responsabilidades

• Investigate and resolve complex escalations in backend systems.
• Participate in an on-call rotation for incident response.
• Implement AI-driven tools for incident triage and log analysis.

Conocimientos

Java

Spring Boot

Kubernetes

RESTful APIs

AI-driven tools

Descripción del empleo

At Backbase, we build AI-powered, secure, enterprise-grade digital banking software used daily by millions worldwide. With seamless integration, unified data, agentic AI, and a complete banking suite built for every line of business, you'll be building the next generation of banking.

The Role

We are seeking an AI Applied Engineer with strong backend software engineering experience to help maintain the health of our production systems by resolving complex escalations and implementing automation. Our core backend relies heavily on Java, Spring Boot, and microservices orchestrated via Kubernetes. In this role, we embrace a T-shaped engineering profile. You will anchor your work in your strong knowledge of the Java ecosystem, while utilizing AI tools to operate across the wider tech stack. Because system stability is a 24/7 priority, our team utilizes a "follow the sun" model, meaning you will participate in an on-call rotation and handle critical incidents outside of standard business hours. Rather than just closing tickets, you will use AI and agentic workflows to optimize our support processes, automate repetitive triage tasks, and implement permanent fixes for underlying system defects. You will be a key contributor who collaborates with senior engineers to raise the bar on how we handle application support in a modern, cloud-native environment.

Key Responsibilities

• End-to-End Troubleshooting: Investigate and resolve complex escalations by debugging Java and Spring Boot code, tracing issues through REST APIs, and identifying bottlenecks in our microservices architecture.
• Global Incident Response: Participate in our "follow the sun" support model, which includes an on-call rotation to acknowledge alerts and troubleshoot high-priority incidents outside of standard business hours.
• AI-Driven Automation: Implement agentic tooling, LLMs, and AI automation to streamline incident triage, log analysis, and repetitive support workflows as you identify system pain points.
• Infrastructure Navigation: Monitor, debug, and support application stability within a Kubernetes-orchestrated containerized environment.
• Technical Collaboration: Work closely with senior engineers, core development, and product teams to elevate critical bugs, test patches, and share knowledge across the team.
• Proactive Problem Solving: Help identify trends in system failures, participate in cross-system investigations, and contribute to code refactoring or automation initiatives to prevent recurrence.

Qualifications

• Experience: 3 to 5 years of professional backend software engineering or highly technical application support experience.
• Core Stack Knowledge: Solid, hands‑on programming and debugging experience with Java and the Spring Boot framework.
• Architecture & Infrastructure: Proven experience working with or troubleshooting RESTful APIs, microservices architectures, and Kubernetes clusters in production.
• Operational Readiness: Willingness and ability to participate in an on-call rotation and respond to out-of-hours emergencies as part of a global "follow the sun" model.
• AI Tooling: Demonstrated experience (or strong aptitude and interest) in utilizing AI-driven tools (e.g., AI coding assistants, automated log analyzers, LLM‑driven scripting) to optimize workflows and automate operational tasks.
• Core Competencies: Strong technical judgment, able to anticipate how a bug fix might impact a distributed system, and communicate trade‑offs clearly to the team.