Description:

LogicMonitor is hiring an Automation Engineer to join its agile development engineering team at its dedicated tech hub in Pune. In this hybrid observability role, you will safeguard the stability, performance, and scalability of LogicMonitor's core AI-first SaaS datacenter monitoring solution. Moving past passive script execution, you will analyze business specs, architect programmatic test coverage, and own the full QA lifecycle across distributed, high-concurrency web systems. Your responsibilities will center on creating functional test plans, performing deep REST API automation sweeps, logging complex bottlenecks using the Atlassian suite, and utilizing modern AI productivity tools to accelerate regression and performance triage.

Key Technical Skills:

Python, Test Automation, Pytest, REST API Automation, SQL, HTTP/HTTPS Protocols, Docker, Kubernetes, Amazon Web Services (AWS), Agile/Scrum Quality Lifecycle, Linux CLI, Data Center Monitoring Protocols (SNMP, WMI), AI Tooling (Cursor, Copilot, Codex), JIRA, Confluence

Requirements:

• Bachelor of Science (BS) degree or higher in Computer Science, Information Technology, or a closely related quantitative engineering field.
• 2+ years of verified professional experience handling enterprise-scale software testing, test analysis, framework design, and regression tracking.
• Solid background testing distributed, high-availability web systems, cloud-native deployments, and SaaS infrastructures.
• Strong programming foundations in Python paired with a deep operational understanding of database interactions using SQL, HTTP network patterns, and command-line execution over Linux platforms.
• Hands-on proficiency setting up and managing programmatic test scripts using open-source automation testing tools (explicitly including Pytest or equivalent REST API automation engines like TestNG).
• Practical baseline exposure navigating container orchestration platforms (Docker and Kubernetes) alongside public cloud architectures (preferably AWS).
• Experience actively utilizing modern AI developer tools (such as Cursor, GitHub Copilot, or OpenAI Codex) to optimize test creation, script generation, and local problem-solving efficiency.
• Prior exposure or architectural understanding of infrastructure monitoring and communication protocols like SNMP and WMI; background reading developer source code modifications to adapt local testing plans; or basic exposure to network programming.