Java Backend Engineer (Production Support)

Role overview
We are looking for a Java Backend Engineer with strong hands-on production support skills to keep our platform healthy: monitoring systems, building operational dashboards, and tracing issues across frontend-to-backend API flows. You will use AI-assisted tools such as Cursor for rapid root-cause analysis and trend spotting, and lead incident resolution with cross-functional teams.
Title: Java Backend Engineer – Production Support · Experience: 5+ years · Type: Full-time · Location: To be confirmed · Shift: To be confirmed (on-call rotation)
Key responsibilities
Production support: Provide L2/L3 support for Java backend services, including on-call; triage and resolve incidents within SLAs; drive major incidents to closure; lead RCA and post-incident reports; apply hotfixes through change management.
Monitoring and observability: Monitor availability, latency, error rates and resource usage; build dashboards and alerts (Grafana, Kibana, Splunk, Datadog, Dynatrace, New Relic); tune alerts to cut noise; track and report SLIs/SLOs.
End-to-end API analysis: Trace requests from web/mobile through API gateways, microservices, queues and databases using logs, distributed tracing and browser tools; analyse contracts, payloads and status codes to isolate frontend vs backend issues.
AI-assisted RCA: Use Cursor, GitHub Copilot or Claude to navigate large codebases, pinpoint faulty code, summarise logs and identify recurring operational trends; validate AI findings with sound judgement and testing.
Collaboration and improvement: Work with development, QA, DevOps/SRE, product and business teams; communicate incident status and impact clearly; automate repetitive tasks and contribute permanent fixes; maintain runbooks and knowledge-base articles.
Required skills and experience
5+ years of Java (8/11/17+) backend development, with 2+ years in hands-on production support.
Spring Boot, Spring MVC/REST and microservices; REST APIs, HTTP, JSON and API gateways, with the ability to trace a request end to end.
SQL on Oracle, PostgreSQL or MySQL, including slow-query and data-issue analysis.
Monitoring and logging with Splunk, ELK/Kibana, Grafana, Prometheus, Datadog, Dynatrace or AppDynamics, including building dashboards and alerts.
Proven use of AI-assisted development tools (Cursor, GitHub Copilot, Claude or similar) for code analysis and debugging, with real examples.
JVM debugging: thread and heap dumps, memory leaks, performance tuning.
ITIL incident management with ServiceNow, PagerDuty or Jira Service Management.
Git, CI/CD (Jenkins, GitHub Actions or GitLab CI), Linux/Unix, and browser dev tools to separate UI and API issues.

Work arrangement
Hybrid