Production Support Engineers

EverAI Labs

We are seeking for experienced SRE/Production Support Engineer to join our dynamic team and ensure the seamless operation of our EverAI Suite products. In this role, you will provide 24/7 production support, troubleshoot issues, monitor system performance, and collaborate with development teams to maintain high availability and reliability. This position is ideal for problem-solvers who thrive in fast-paced environments and are passionate about AI technologies.

Key Responsibilities

• Monitor production environments for EverAI Suite products (EverAI Simulator, EverAI Recruiter, and EverAI Knowledgeminer) using tools like Splunk, Prometheus, Grafana, ELK Stack, or similar monitoring systems.
• Respond to incidents, alerts, and user-reported issues in a timely manner, performing root cause analysis and implementing fixes or workarounds.
• Collaborate with cross-functional teams (development, QA, and operations) to resolve complex production problems and prevent recurrence.
• Maintain and update documentation for support processes, troubleshooting guides, and knowledge bases.
• Perform routine maintenance tasks, such as patching, scaling resources, and optimizes performance in cloud-based infrastructures (e.g., AWS, Azure, or GCP).
• Participate in on-call rotations to provide after-hours support and ensure SLAs are met.
• Analyze logs, metrics, and traces to identify trends, potential bottlenecks, and areas for improvement.
• Assist in deployment activities, including CI/CD pipeline support and rollback procedures.
• Contribute to continuous improvement initiatives, such as automating support tasks and enhancing monitoring capabilities.

Required Qualifications

• Bachelor’s degree in computer science, Information Technology, Engineering, or a related field (or equivalent experience).
• 3+ years of experience in production support, DevOps, or site reliability engineering (SRE) roles.
• Strong troubleshooting skills with experience in debugging distributed systems, APIs, and microservices architectures.
• Proficiency in scripting languages such as Python, Bash, or PowerShell for automation.
• Hands-on experience with cloud platforms (AWS, Azure, GCP) and containerization tools (Docker, Kubernetes).
• Familiarity with monitoring and logging tools (e.g., Splunk, Datadog, New Relic).
• Knowledge of databases (SQL/NoSQL) and networking concepts.
• Excellent communication skills, with the ability to explain technical issues to non-technical stakeholders.
• Ability to work in a shift-based or on-call environment.

Preferred Qualifications

• Experience supporting AI/ML-based products or SaaS platforms.
• Certifications such as AWS Certified DevOps Engineer, Google Cloud Professional SRE, or equivalent.
• Familiarity with incident management frameworks (e.g., ITIL) and tools like PagerDuty or Jira.
• Strong problem-solving mindset with a proactive approach to preventing issues.

If you’ve got the skills to succeed and the motivation to make it happen, we look forward to hearing from you.