Engineer, Applied AI

About the role

We build and use automation every day to make work more efficient, creative, and human. This role involves building and evolving the shared infrastructure that powers AI and ML development across Zapier.

Responsibilities

• Help build and evolve the shared infrastructure that powers AI and ML development across Zapier.
• Create the core systems, tooling, and standards that many teams use as their baseline for shipping intelligent products and internal AI-powered workflows.
• Work at the intersection of platform engineering, applied AI, and developer experience to make it easier for teams across Zapier to build with LLMs and ML systems in a scalable, secure, and production-ready way.
• Focus heavily on LLM Ops and ML Ops: improving how models are accessed, monitored, evaluated, deployed, governed, and operated in production.
• Help define the paved road for teams building with AI at Zapier.
• Contribute to shared AI Platform capabilities that support teams building with machine learning and generative AI across Zapier.
• Work mostly in TypeScript & Python. Experience isn’t strictly required, but it is a big plus.
• Develop and maintain core services such as our LLM proxy server, platform APIs, and reusable tooling that standardize how teams access and operate models in production.
• Build and improve parts of our LLM Ops and ML Ops stack, including observability, monitoring, evaluation workflows, and operational tooling.
• Collaborate closely with engineers across product, infra, and data teams to ensure our AI components are reusable, well-documented, and easy to adopt company-wide.
• Evaluate emerging tools, models, and patterns in the AI ecosystem, and help determine which ones should be incorporated into Zapier’s shared platform.

Requirements

• 4+ years of experience in software engineering, including experience building and operating production AI/ML systems.
• Solid engineering fundamentals, good communication skills, and a desire to build reliable systems that others can depend on.
• At least 1 year of experience in LLM Ops, ML Ops, or adjacent platform/infrastructure work.
• Experience contributing to backend systems, developer tooling, internal platforms, or infrastructure that supports other engineers.
• Experience of working through the full lifecycle of building, testing, deploying, and scaling ML/LLM architectures.
• Thoughtful about engineering trade-offs and developing a strong understanding of how to balance reliability, latency, cost, quality, and maintainability in production systems.
• Enjoy working collaboratively and learning from others.
• Excited to partner with more senior engineers and cross-functional teams to build reusable platform capabilities that make AI and ML development easier across the company.

Qualifications

• Experience in TypeScript & Python.
• Comfort with typed languages and modern backend practices.
• Experience in LLM Ops, ML Ops, or related fields.
• Experience in evaluating and incorporating emerging tools, models, and patterns in the AI ecosystem.