Java + SRE Engineer
Tech3pillars Technologies
Phoenix, AZ, United States
11 days ago
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Role details
Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
5 years minimum
Working hours
Regular working hours
Job source
Tech stack
Java (Programming Language)
Artificial Intelligence
Unit Testing
Cloud Computing
Continuous Integration
Software Debugging
Linux
DevOps
Elasticsearch
Java Platform Enterprise Edition (J2EE)
Linux System Administration
Log Analysis
+14 more
OpenShift
Reliability Engineering
GitHub Copilot
Delivery Pipeline
Large Language Models
Prompt Engineering
Spring-boot
Generative AI
AI Coding Agents
Kubernetes
Kibana
Restful APIs
Splunk
Microservices
Job description
- Java Engineering: Develop, enhance, and troubleshoot enterprise microservices built on Java/J2EE, Spring Boot, and REST APIs.
- SRE & Production Operations: Manage production incidents, perform Root Cause Analysis (RCA), and drive reliability, availability, and SLA/SLO performance.
- Observability & Diagnostics: Monitor platform health and analyze logs using Splunk, Elasticsearch, or Kibana.
- Platform & DevOps Support: Support applications running on Kubernetes/OpenShift and Linux, and manage CI/CD deployment pipelines.
- AI-Assisted Engineering: Utilize GenAI tools and LLM coding assistants (GitHub Copilot) for log analysis, code comprehension, unit testing, and operational automation.
Required Technical Skills:
- Core Tech: Java, Spring Boot, REST APIs, Microservices.
- SRE / Monitoring: Production Support, RCA, Splunk, Kibana, SLA/SLO tracking.
- Cloud & Platform: Kubernetes/OpenShift, CI/CD, Linux environments.
- AI & GenAI: Practical experience with GitHub Copilot/AI coding tools, prompt engineering basics, and leveraging LLMs for code debugging and automation.
Requirements
We are looking for a hands-on Java + SRE Engineer who combines strong application engineering skills with a site reliability mindset. In this role, you will develop, troubleshoot, and support production microservices while leveraging Generative AI and AI coding assistants (e.g., GitHub Copilot) to accelerate debugging, log analysis, automation, and solution delivery.
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