Quick overview
Job description
- We are looking for a hands-on Java + SRE Engineer with strong application development, production support, observability, and troubleshooting experience.
- The candidate should have solid Java/Spring Boot fundamentals along with practical knowledge of Generative AI and AI-assisted engineering tools to accelerate development, problem solving, automation, and learning of new technologies.
- Develop, enhance, troubleshoot, and support enterprise applications using Java/J2EE, Spring Boot, REST APIs, and microservices.
- Provide SRE and production engineering support for business-critical applications and services.
- Troubleshoot complex production issues across application, API, infrastructure, database, and network layers.
- Perform Root Cause Analysis (RCA) and drive corrective and preventive actions for recurring incidents.
- Support Incident, Problem, and Change Management processes and work with application SLA/SLOs.
- Monitor application and platform health using Splunk, Elasticsearch/Kibana, or similar observability platforms.
- Create and enhance dashboards, alerts, log queries, and health monitoring.
- Support applications running on Kubernetes/OpenShift and enterprise Linux environments.
- Work with CI/CD pipelines, release/deployment processes, and production validation.
- Identify opportunities to automate repetitive operational activities and improve application reliability.
- Use AI-assisted engineering tools appropriately for code understanding, debugging, log analysis, documentation, testing, and automation.
- Quickly ramp up on new applications, technologies, APIs, and support processes based on project requirements.
- Strong hands-on experience with Java/J2EE.
- Strong knowledge of Spring Boot, REST APIs, and microservices.
- Experience debugging production Java applications, including logs, exceptions, API failures, and performance issues.
- Understanding of application design, integrations, and distributed systems.
- Strong production support and troubleshooting experience.
- Knowledge of Incident, Problem, and Change Management.
- Experience with RCA, SLA/SLO, application availability, reliability, and production stability.
- Ability to identify recurring operational issues and implement engineering or automation-based solutions.
- Hands-on experience with Splunk.
- Knowledge of Elasticsearch/Kibana or similar observability tooling.
- Experience with application monitoring, dashboards, alerts, log analysis, and production diagnostics.
- Cloud / Platform / DevOps
- Good understanding of Kubernetes/OpenShift.
- Knowledge of CI/CD pipelines, application deployments, and release management.
- Understanding of networking, certificates, application connectivity, and traffic-routing concepts.
- Scripting/automation experience is preferred.
- AI / GenAI Knowledge
- Working knowledge of Generative AI, LLMs, and AI-assisted software engineering.
- Experience using tools such as GitHub Copilot or equivalent AI coding assistants.
- Ability to effectively use AI for:
- Code understanding and development
- Debugging and troubleshooting
- Log and error analysis
- Unit/test-case generation
- Technical documentation
- Script and automation development
- Understanding unfamiliar applications and codebases
- Basic understanding of prompt engineering and effective interaction with LLM-based tools.
- Awareness of RAG, AI agents/agentic AI, and AI-assisted automation is a plus.
- Ability to critically review and validate AI-generated code or recommendations before using them in production.
- A strong Java engineer with an SRE mindset who can work across development and production environments.
- The candidate should be comfortable moving between code-level debugging, APIs, observability, Kubernetes/OpenShift, production incidents, RCA, and automation.
- The candidate should also be comfortable using AI as an engineering accelerator, while having sufficient Java and SRE fundamentals to independently validate solutions and rapidly ramp up on unfamiliar technologies.
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