Site Reliability Engineer II

Site Reliability Engineers (SREs) are responsible for supporting and improving the reliability of production systems. In this role, you will work hands-on with real systems, contribute to incident response, and develop the technical skills needed to operate and troubleshoot modern distributed applications.

We are looking for engineers who are proactive, detail-oriented, and eager to take on technical challenges. This role is ideal for candidates who want to build a strong foundation in systems, automation, and reliability engineering.

Monitoring & Observability: 

• Monitor system health using metrics, logs, and traces.

• Respond to alerts and perform initial investigation using runbooks.

• Identify patterns in alerts and contribute to improving alert quality and signal.

Incident Support & Troubleshooting:

• Participate actively in incident response and assist in troubleshooting issues.

• Perform initial diagnosis by analyzing logs, metrics, and system behavior.

• Escalate issues with clear context and supporting data.

• Contribute to incident documentation and post-incident reviews.

Operations & System Support:

• Support operational tasks such as deployments, rollbacks, and system checks.

• Assist in maintaining production systems by identifying and reporting anomalies or risks.

• Follow and improve standard operating procedures (SOPs) and runbooks.

Automation & Improvement:

• Develop scripts or small tools to automate repetitive operational tasks.

• Contribute to improving monitoring, alerting, and operational workflows.

• Identify inefficiencies and suggest improvements to enhance reliability.

 Performance & System Understanding:

• Assist in analyzing system performance and identifying basic bottlenecks.

• Develop an understanding of how services interact and fail in production.

• Support troubleshooting efforts across application and infrastructure layers.

Collaboration & Learning:

• Work closely with developers and cross-functional teams.

• Communicate clearly on findings, issues, and progress.

• Continuously learn from incidents, system behavior, and team feedback.

AI & Learning (Nice to Have / Exposure):

• Gain exposure to AI/ML tools used in monitoring and operations.

• Explore how AI can assist in anomaly detection and incident analysis.

• Show curiosity in AIOps and automation-driven operations.

Technical Skills

• Good understanding of system and networking concepts (HTTP, DNS, TCP/IP).

• Ability to write code or scripts (e.g., Python, JavaScript, or similar) for basic automation.

• Ability to analyze logs and metrics to support troubleshooting.

• Basic understanding of how web applications and services operate in production.

• Willingness to learn and work with observability tools (e.g., Datadog).

Education / Background

• Bachelor’s degree in Computer Science, Information Technology, or a related field, or equivalent practical experience.

• Minimum 1–3 years of experience in software engineering, DevOps, or related areas (including internships or projects).

Nice to Have

• Internship or project experience involving backend systems, APIs, or cloud platforms.

• Exposure to cloud environments (AWS, GCP, or Azure).

• Basic understanding of CI/CD pipelines and deployment workflows.

• Basic knowledge or hands-on experience with AI/ML concepts or tools.

• Interest in applying AI to monitoring, automation, or operational efficiency.

Soft Skills

• Strong willingness to learn and take on technical challenges.

• Problem-solving mindset with attention to detail.

• Ability to communicate clearly and work collaboratively.

• Proactive attitude and sense of ownership for assigned tasks.