Dutch is transforming veterinary care by making expert treatment accessible anytime, anywhere. Our mission is simple: help every pet live their happiest, healthiest life by connecting pet parents with licensed vets through seamless virtual visits.
Backed by world-class investors including Forerunner Ventures, Eclipse Ventures, and Bling Capital, our team includes successful startup founders (Hims, Plush Care, Nasty Gal) with expertise in scaling enterprises (Trip Advisor, Walmart, BARK). Featured in Tech Crunch, Forbes, Wired, and Axios, Dutch is setting the standard for quality, accessibility, and compassion in pet care.
The Role We're looking for a hands-on Lead AI/Data Engineer to build and scale the AI systems powering Dutch. You'll design and ship production AI agents and services for pet parents, veterinarians, and internal teams. You'll also own the underlying AI infrastructure, including evaluation frameworks, prompt optimization, observability, and cost management.
You'll help shape our technical approach to AI development, set engineering standards, and partner with product and engineering teams to bring recent capabilities into production. This role also owns key parts of our data platform. You'll build and maintain data pipelines, warehouse models, and analytics and experimentation infrastructure that support our AI systems and broader business needs.
WHAT YOU'LL DO: AI Services & Platform Tooling (60%) Design, build, and operate production AI agents and services for pet parents, veterinarians, and internal teams, grounded in our data and built with appropriate safeguards.
Own the AI development infrastructure, including evaluation frameworks, prompt optimization, LLM observability, and cost management.
Establish engineering standards and reusable patterns for retrieval, tool calling,



guardrails, and model selection.
Design and run systematic experiments across models, prompts, retrieval strategies, and agent architectures. Build evaluation datasets and automated tests to measure quality, safety, performance, and cost, and detect regressions.
Evolve the semantic layer that enables AI systems to answer questions about our business data accurately.
Work closely with product engineering to deliver AI capabilities through APIs and integrate them into production applications.
Build and maintain reliable ELT pipelines and dbt models in Snowflake, using Prefect for orchestration and Fivetran and Airbyte for data ingestion.
Develop trusted data marts and datasets that support AI features, product analytics, and experimentation.
Build the data foundation for experimentation, including consistent metrics, baselines, and feature-impact measurement.
Maintain automated data quality tests, freshness checks, and alerts for critical pipelines.
Diagnose pipeline failures, address root causes, and improve reliability.
Support warehouse security, governance, performance, and cost management.
Event Tracking & Product Analytics (15%) Build and maintain reliable event data pipelines from Segment into Amplitude, Iterable, and Snowflake.
Partner with product engineering to define and validate event-tracking plans for new features, ensuring accurate, consistent instrumentation.




Diagnose and resolve identity resolution, user property, and event-tracking issues affecting AI systems and product analytics.
Support product experimentation through reliable event tracking, consistent metrics, and accurate measurement of experiment outcomes.
Build and maintain Amplitude dashboards and reports that help product teams understand user behavior and measure feature performance.
WHAT YOU'LL BRING: 6+ years of experience in software, data, or ML engineering , including 2+ years building and operating LLM applications in production.
Strong Python and SQL skills, with experience building reliable, maintainable production systems.
Hands-on experience developing AI agents and applications using LLM APIs, agent frameworks, tool calling, retrieval, and structured outputs.
Experience designing and running LLM evaluations, including test datasets, automated graders, regression testing, and systematic experimentation.
Experience with LLM observability, performance optimization, and cost management in production.
Experience deploying and operating services on AWS, GCP, or Azure, using containers and CI/CD pipelines.
Working knowledge of modern data platforms, including Snowflake or similar warehouses, dbt, and orchestration tools such as Prefect, Airflow, or Dagster.
Familiarity with product analytics and event tracking platforms such as Segment, Rudder Stack, and Amplitude, or a willingness to learn.
Familiarity with BI tools such as Sigma, Looker, or Tableau.
Bonus: Experience with prompt optimization, semantic layers, data contracts, or A/B testing and experimentation infrastructure.
WHY YOU'RE A GREAT FIT: You've built and operated production AI system

📌 Lead ai/data engineer (Vancouver)
🏢 Dutch Vet
📍 Vancouver