Engineer, Applied AI

Zapier's AI Platform team owns the shared infrastructure that powers AI and machine learning development across the company, including the LLM proxy server, observability tooling, and ML Ops platform capabilities. As an Applied AI Engineer you will build the core systems, tooling, and standards that many teams use as their baseline for shipping intelligent products and internal AI-powered workflows. This is a highly leveraged role at the intersection of platform engineering, applied AI, and developer experience, focused on improving how models are accessed, monitored, evaluated, deployed, governed, and operated in production.

Responsibilities

• Contribute to shared AI Platform capabilities that support teams building with machine learning and generative AI across Zapier, working mostly in TypeScript and Python.
• Help develop and maintain core services such as the LLM proxy server, platform APIs, and reusable tooling that standardize how teams access and operate models in production.
• Build and improve the LLM Ops and ML Ops stack, including observability, monitoring, evaluation workflows, and operational tooling.
• Design and implement systems that improve the performance, reliability, safety, and cost efficiency of AI-powered experiences.
• Collaborate closely with engineers across product, infrastructure, and data teams to ensure 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 should be incorporated into Zapier's shared platform.

Qualifications

• 4+ years of software engineering experience, including building and operating production AI/ML systems.
• At least 1 year of experience in LLM Ops, ML Ops, or adjacent platform and infrastructure work, with interest in the practical challenges of reliability, performance, safety, and cost.
• Experience contributing to backend systems, developer tooling, internal platforms, or infrastructure that supports other engineers.
• Experience working through the full lifecycle of building, testing, deploying, and scaling ML and LLM architectures.
• Thoughtful about engineering tradeoffs, balancing reliability, latency, cost, quality, and maintainability in production systems.
• Collaborative and eager to partner with senior engineers and cross-functional teams to build reusable platform capabilities.