Zuora powers the world’s shift to Modern Business. We’re helping people and companies subscribe to a better way of doing business—one that’s built on recurring relationships instead of one-time transactions, creating more value for customers, companies, and the planet. As pioneers of the Subscription Economy, our platform and expertise help the world’s most innovative organizations—from disruptive startups to global enterprises—monetize new business models, nurture long-term subscriber relationships, and optimize their digital experiences. At Zuora, we’re constantly learning, innovating, and growing. Our people—known as ZEOs—are empowered to take ownership, challenge the status quo, and make a lasting impact. We collaborate deeply, think boldly, and support one another to make what’s next possible—for our customers, our communities, and each other.
Senior AI Engineer
Location
United States
Posted
74 days ago
Salary
$124.5K - $171.3K / year
Seniority
Senior
Job Description
Senior AI Engineer
Zuora
• Design and build the glue between AI agents and core business systems such as Salesforce, NetSuite, Slack, and other internal platforms using APIs, events, and webhooks • Leverage modern AI coding assistants such as Cursor and Codex to accelerate development, generate boilerplate, debug issues, and refactor quickly • Rapidly prototype and ship AI-powered internal tools in a high-velocity environment, moving from concept to production with strong engineering judgment • Parse, understand, and enhance existing internal codebases, integrating AI capabilities into brownfield systems without disrupting production workflows • Architect advanced RAG pipelines and agentic workflows that bring context-aware intelligence to internal knowledge and operational systems • Implement rigorous evaluation and testing approaches for AI outputs to ensure speed does not compromise accuracy, security, or trust • Collaborate with stakeholders across the enterprise to deliver solutions that improve how work gets done • Assist colleagues with design and coding best practices, including code reviews, documentation, and maintainable implementation patterns • Partner with Enterprise Architecture and adjacent teams to ensure AI integrations align with the broader digital ecosystem • Lead troubleshooting for production issues in AI-enabled workflows, perform root-cause analysis, and help build support-ready systems
Job Requirements
- 8+ years of relevant work experience and a Bachelor’s degree in Computer Science or a related technical discipline
- 5+ years of experience as a full-stack developer
- Strong experience building integrations with REST APIs, webhooks, and middleware technologies
- Demonstrated expertise in AI-augmented programming and vibe coding, using LLMs to write code, explore new libraries quickly, and automate repetitive engineering work
- Exceptional ability to jump into existing codebases in Python and JavaScript/TypeScript, understand the logic, and implement enhancements or integrations safely
- Familiarity with LangChain, LlamaIndex, vector databases, prompt engineering, and RAG design patterns
- Experience designing or contributing to agentic workflows and context-aware AI applications
- Strong problem-solving instincts and the ability to figure out how systems work without waiting for perfect documentation
- Excellent listening, communication, and presentation skills
- The ability and desire to learn new technologies and development tools
Benefits
- Competitive compensation, variable bonus and performance-based reward opportunities, and retirement programs
- Medical, dental, and vision insurance
- Generous, flexible time off, plus paid holidays, wellness days, and a company-wide year-end break
- Paid parental leave (including fully paid leave for eligible ZEOs, subject to local policy)
- Learning & development stipend to support ongoing growth
- Opportunities to volunteer and give back, including charitable donation matching where available
- Mental wellbeing resources and support
Related Guides
Related Job Pages
More AI Engineer Jobs
Senior Software Engineer, AI
GFT TechnologiesAs a pioneer for digital transformation GFT develops sustainable solutions across new technologies.
• Build a centralized reliability command center that gives teams a unified view of system health, risk, and reliability across services and environments • Create AI agents that can assist with incident triage, root-cause exploration, log/trace summarization, and recommended next actions • Create developer-facing features and APIs that help engineers explore data, debug issues, and make better decisions • Use AI-assisted development tools (e.g., Cursor, Claude, Copilot) as leverage to prototype, refactor, and ship high-quality code quickly • Own projects end-to-end: requirements, architecture, implementation, testing, rollout, and iteration based on feedback • Collaborate closely with partner teams (Product, Infra, Data, SRE) to understand pain points and translate them into simple, powerful solutions
Software Engineer, AI
GFT TechnologiesAs a pioneer for digital transformation GFT develops sustainable solutions across new technologies.
• Build a centralized reliability command center that gives teams a unified view of system health, risk, and reliability across services and environments • Create AI agents that can assist with incident triage, root-cause exploration, log/trace summarization, and recommended next actions • Create developer-facing features and APIs that help engineers explore data, debug issues, and make better decisions • Use AI-assisted development tools (e.g., Cursor, Claude, Copilot) as leverage to prototype, refactor, and ship high-quality code quickly • Own projects end-to-end: requirements, architecture, implementation, testing, rollout, and iteration based on feedback • Collaborate closely with partner teams (Product, Infra, Data, SRE) to understand pain points and translate them into simple, powerful solutions
• Evaluate, implement, and guide the effective use of AI tools within the software development lifecycle. • Establish best practices for AI development, coding assistance, coding agents, AI testing, documentation, debugging, and engineering workflow optimization. • Train engineers to become "architects of intent" rather than just writers of code—focusing on providing clear, high-level, context-rich goals, constraints, and validation criteria for AI agents to execute. • Institutionalize a culture of validation, requiring human engineers to thoroughly review, test, and understand AI-generated code to reduce risk of production issues. • Deploy multi-agent workflows using dedicated agent teams (e.g., separate agents for planning, coding, and testing) operating in parallel to automate end-to-end tasks like PR generation, refactoring, or legacy modernization. • Integrate agents directly into error monitoring systems and bug reporting (Jira) to automatically ingest stack traces, locate the root cause across the codebase, and generate a verified PR with a corresponding regression test prior to engineering triaging the ticket. • Prioritize refactoring for high modularity, deterministic testing, and explicit documentation to ensure agents can navigate, understand, and safely modify code, treating codebase health as the foundation for AI capability. • Mentor members of the Engineering team, supporting their growth, accountability, and day-to-day effectiveness using AI tools. • Partner closely with Product Management, CX leaders, and other stakeholders to translate business needs into high-quality technical solutions. • Ensure AI development meets a high bar for software quality, security, scalability, and reliability. • Design scalable AI/ML pipelines using LLMs, RAG, and agentic frameworks and integrate AI APIs into customer-facing applications and workflows. • Develop reusable accelerators, templates, and reference architectures to be leveraged by the broader engineering team.
• Evaluate, implement, and guide the effective use of AI tools within the software development lifecycle. • Establish best practices for AI development, coding assistance, coding agents, AI testing, documentation, debugging, and engineering workflow optimization. • Train engineers to become "architects of intent" rather than just writers of code—focusing on providing clear, high-level, context-rich goals, constraints, and validation criteria for AI agents to execute. • Institutionalize a culture of validation, requiring human engineers to thoroughly review, test, and understand AI-generated code to reduce risk of production issues. • Deploy multi-agent workflows using dedicated agent teams (e.g., separate agents for planning, coding, and testing) operating in parallel to automate end-to-end tasks like PR generation, refactoring, or legacy modernization. • Integrate agents directly into error monitoring systems and bug reporting (Jira) to automatically ingest stack traces, locate the root cause across the codebase, and generate a verified PR with a corresponding regression test prior to engineering triaging the ticket. • Prioritize refactoring for high modularity, deterministic testing, and explicit documentation to ensure agents can navigate, understand, and safely modify code, treating codebase health as the foundation for AI capability. • Mentor members of the Engineering team, supporting their growth, accountability, and day-to-day effectiveness using AI tools. • Partner closely with Product Management, CX leaders, and other stakeholders to translate business needs into high-quality technical solutions. • Ensure AI development meets a high bar for software quality, security, scalability, and reliability. • Design scalable AI/ML pipelines using LLMs, RAG, and agentic frameworks and integrate AI APIs into customer-facing applications and workflows. • Develop reusable accelerators, templates, and reference architectures to be leveraged by the broader engineering team.
