AI Site Reliability Engineer $100,000 USD/year Pay is set based on global value, not the local market.
Most roles = hourly rate x 40 hrs x 50 weeks
Description
Traditional site reliability teams often find themselves trapped in cycles of manual monitoring, reactive incident response, and deployment processes that demand constant human oversight. While AI technology offers a clear path forward, most organizations fail to harness it effectively, leading to suboptimal system performance and operational bottlenecks that impede progress. Research indicates that 73% of organizations experience deployment delays and operational downtime, largely attributable to legacy processes and insufficient AI-powered automation.
IgniteTech is addressing these challenges directly by developing cloud solutions that place AI at the foundation, designed to predict and eliminate issues before they materialize. Our approach embeds AI and machine learning throughout cloud infrastructure management—spanning automated monitoring frameworks to intelligent CI/CD pipelines. The result is infrastructure that self-heals and continuously adapts, minimizing downtime, enhancing performance, and expanding the capabilities of cloud services.
This position differs fundamentally from conventional site reliability roles that center on reactive problem-solving and manual intervention. In this capacity, you'll spearhead the development of AI-enhanced monitoring systems capable of detecting and resolving 95% of incidents before users are affected. You'll also design and oversee AI-automated CI/CD pipelines that cut deployment times by 30% while dramatically reducing manual touchpoints. The right candidate excels in AI-driven settings, embraces automation-first methodologies, and seeks to advance the frontiers of cloud infrastructure architecture.
You'll become part of a global team of innovators reshaping cloud infrastructure. Your contributions will be central to our objective of delivering next-generation, AI-powered operational excellence. We're looking for someone who is deeply invested in AI and prepared to influence the future of cloud services in meaningful ways. If this describes you, we invite you to apply and contribute to a transformative initiative.
What you will be doing
- Deploying AI-based monitoring services that automatically detect, predict, and remediate issues before operational impact occurs
- Overseeing CI/CD pipelines enhanced by AI-driven automation to improve deployment efficiency and minimize manual intervention
What you will NOT be doing
- Concentrating exclusively on manual monitoring, troubleshooting, and system maintenance; instead, your objective will be enabling AI to handle these functions
Key responsibilities
- Deliver seamless scalability and performance optimization for AI-powered cloud services, maintaining 99.99% uptime while executing AI-enhanced software upgrades and customizations that address clients' changing requirements
Candidate requirements
- AI-First Mindset (if your default approach is to write code first and then apply AI tools for verification or enhancement, rather than the reverse, please do not apply)
- A minimum of 3 years of DevOps experience, encompassing automation of CI/CD pipelines and infrastructure management
- A minimum of 2 years of hands-on experience with Amazon Web Services (AWS) or Google Cloud Platform (GCP)
- Proficiency in AI and machine learning tools applied to monitoring, automation, and predictive analytics (or a strong commitment to learning and adapting to AI-driven technologies)
- Solid programming and scripting capabilities, with demonstrated experience automating tasks and constructing AI-driven processes
Applying for a role? Here’s what to expect.
Crossover's skill assessment process combines innovative AI power with decades of human research, to take the guesswork, human bias, and pointless filters out of recruiting high-performing teams.
Chat-style screening interview.
Cognitive aptitude test.
Prove real-world job skills.
Interview with the hiring manager.
Accept job offer.
Pass proctored test.
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