JD:
Role Summary
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.
Key Responsibilities
- 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.
Required Technical Skills
Java / Application Engineering
- 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.
SRE / Production Engineering
- 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.
Observability
- 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.
Preferred Candidate Profile :
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.
