Lead AI and Data Engineer in Vancouver

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, establish engineering standards, and work closely with product and engineering teams to bring new capabilities into production. This role also includes ownership of key parts of our data platform.

• 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 measurement of feature impact.
• 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.
• 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 and consistent instrumentation.
• Diagnose and resolve identity resolution, user property, and event tracking issues that affect 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.
• Hands-on experience developing AI agents and applications using LLM APIs, agent frameworks, tool calling, retrieval, and structured outputs.
• You use evaluations, experiments, and data to guide decisions and continuously improve system performance.
• You thrive in a small team, set priorities independently, and make progress with limited direction.
• You take ownership of technical decisions, from architecture and implementation through deployment and ongoing operations.
• Familiarity with BI tools such as Sigma, Looker, or Tableau.
• You work effectively across data, product, and engineering teams, and understand how to integrate AI capabilities into existing applications.
• Experience deploying and operating services on AWS, GCP, or Azure, using containers and CI/CD pipelines.
• Familiarity with product analytics and event tracking platforms such as Segment and Amplitude, or a willingness to learn.
• Experience designing and running LLM evaluations, including test datasets, automated graders, regression testing, and systematic experimentation.
• You’re comfortable working across AI and data engineering, including pipelines, data models, and analytics infrastructure.
• You love animals.
• Working knowledge of modern data platforms, including Snowflake or similar warehouses, dbt, and orchestration tools such as Prefect, Airflow, or Dagster.
• Experience with LLM observability, performance optimization, and cost management in production.
• You build reusable tools, frameworks, and infrastructure that help teams develop and ship AI capabilities faster.
• You’ve built and operated production AI systems that people rely on, and you know when AI is the right solution to a problem.
• Strong Python and SQL skills, with experience building reliable, maintainable production systems.
• You communicate technical decisions clearly and document your work so others can build on it.
• 6+ years of experience in software, data, or ML engineering, including 2+ years building and operating LLM applications in production.
• Experience with prompt optimization, semantic layers, data contracts, or A/B testing and experimentation infrastructure.