Senior System Engineer
Confirmed on the employer's own hiring board on Sep 27, 2026. First seen by Alion on Sep 26, 2026.
About ScienceLogic…
ScienceLogic is redefining IT operations for the modern enterprise. Our AIOps platform empowers organizations to achieve Autonomic IT - where systems are self-healing, self-optimizing, and seamlessly aligned with business outcomes. We help enterprises and service providers gain unified visibility across hybrid and multi-cloud environments, automate workflows, and unlock performance at scale.
We’re accelerating digital transformation through the power of automation, AI, and analytics - giving IT and business leaders the tools to deliver superior customer experiences, drive efficiency, and innovate with confidence.
What we’re looking for...
ScienceLogic is looking for a Senior Systems Engineer to drive the development, automation, and optimization of our hybrid cloud monitoring solutions. The ideal candidate will have a deep understanding of system architecture, cloud infrastructure, and automation tools to ensure our platform operates with high availability, scalability, and efficiency. As a Senior Systems Engineer, you will play a crucial role in designing, implementing, and maintaining our AI/ML-powered hybrid cloud monitoring platform, SkylarOne.
What you’ll be doing...
As a Senior Systems Engineer at ScienceLogic, you will work alongside elite engineers to design, deploy, and maintain our state-of-the-art monitoring solutions. Your role will focus on platform modernization, upgrading platform with latest software, and system reliability in a hybrid cloud environment.
Design and implement scalable and resilient system update software to support the SkylarOne hybrid cloud monitoring platform run latest software.
Drive SkylarOne platform modernization initiatives such as OS migrations and technology stack upgrades.
Troubleshoot complex system issues across distributed environments, identifying root causes and implementing effective solutions.
Optimize system performance, reliability, and scalability by analyzing key metrics and implementing best practices.
Participate in Agile Scrum teams to drive continuous improvements in system operations.
Qualities you possess...
Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field.
Strong experience with AI driven software development practices.
5+ years of experience in systems engineering.
Strong expertise in Linux administration, system and network troubleshooting.
Proficiency in automation and rpm package management tools (dnf, yum etc).
Strong scripting skills in Python and Bash.
Experience with Golang is desired but not mandatory.
Experience with SQL Databases.
Understanding of networking protocols, security best practices, and monitoring tools.
Ability to fluidly change context and priorities based on business needs.
· A passion for problem-solving, attention to detail, and a drive for innovation.
· Demonstrate strong teamwork skills by actively contributing to group efforts, supporting colleagues, and fostering a positive and cooperative work environment.
Don’t meet every single requirement? Studies have shown that women and people of color are less likely to apply to jobs unless they meet every single qualification. At ScienceLogic, we are dedicated to building a diverse, inclusive and authentic workplace, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, or any other applicable legally protected characteristics in the location in which you are applying.
About ScienceLogic
ScienceLogic is a leader in IT Operations Management, providing modern IT operations with actionable insights to predict and resolve problems faster in a digital, ephemeral world. Its solution sees everything across cloud and distributed architectures, contextualizes data through relationship mapping, and acts on this insight through integration and automation.