AI Operations Engineer, Americas

About CodeRabbit

Locations: San Fransisco, CA or Boston, MA (Boston preferred)- Hybrid

Role Overview

At CodeRabbit, we seek an exceptional AI Operations Engineer who identifies operational gaps, designs solutions autonomously, and executes end-to-end. You will own the full lifecycle, from spotting inefficiencies in how support operates, to architecting the fix, to building and deploying the solution yourself. You'll be hands-on with code, automation, integrations, and data systems. You'll think strategically about frameworks and processes, then immediately build what's needed without waiting for someone else. You work across Product, Engineering, Services, and Support to understand pain points, but you drive the technical solutions yourself. This is a role for someone who sees a problem, owns it completely, and ships.

Key Responsibilities

• Identify gaps in support operations and autonomously design, build, and deploy automation systems and AI-powered workflows that improve team productivity, customer satisfaction, and support quality
• Spot inefficiencies in how support operates and build the operational frameworks—automated playbooks, process integrations, quality systems—that fix them at scale
• Autonomously build dashboards, metrics systems, and data pipelines that expose operational gaps and guide improvements
• Own the full problem-to-solution cycle: identify recurring issues, architect technical fixes, and execute the automation, integrations, and tooling yourself
• Build and own the knowledge management system from the ground up—automated runbooks, playbooks, FAQs, documentation—that prevents issues before they happen
• Engineer and deploy AI-powered workflows and automation end-to-end: identify where they're needed, build them, integrate them, optimize them
• Evaluate, architect, and implement the support tooling stack—independently making decisions and building integrations that scale operations
• Own technical initiatives end-to-end: identify the problem, prototype the solution, validate it works, deploy it, optimize it—all autonomously
• Autonomously build systems that improve support quality: quality automation, specialization tooling, knowledge distribution, training frameworks
• Identify and execute supportability improvements autonomously; partner with Engineering and Product to shape AI-driven support
• Drive strategic projects to identify, implement, and scale tools and automation that improve support efficiency, leverage LLM/AI capabilities, and enhance customer experience

Qualifications

Great candidates often have these

qualifications

• At least 3 years of hands-on experience building support operations systems, automation, or technical solutions autonomously in a SaaS environment
• Proven track record of identifying operational problems and autonomously building and deploying solutions that measurably improved support metrics
• Strong technical foundation with proficiency in Python, JavaScript, or similar languages; able to independently build automation, data systems, dashboards, and ship solutions
• Customer Obsession: a passion to deliver a high-quality customer experience
• Systems thinking: ability to see operational gaps, architect fixes autonomously, and execute end-to-end without waiting for someone else to build

“Nice to Have”

Great candidates may also have one or more of these

qualifications

• Proficiency in at least one programming or scripting language
• Experience with code review processes and tools
• Familiarity with observability, RCA, and debugging practices and tools
• Strong understanding of modern software development practices, version control systems, and the software development ecosystem, e.g. Git, GitHub, CI/CD, VS Code, Cursor
• Knowledge of customer success, sales, and professional services (e.g. retention, expansion, MEDDPICC, services delivery)
• Bachelor’s degree (or equivalent) in computer science, engineering, mathematics, or another technical field