Lead AI/Data Engineer

Role overview

AI services and platform

• Build and operate production AI agents and services for pet parents, veterinarians, and internal teams with appropriate safeguards.
• Own evaluation frameworks, prompt optimization, LLM observability, and cost management.
• Create reusable standards for retrieval, tool calling, guardrails, and model selection.
• Run systematic experiments and build evaluation data and tests for quality, safety, performance, cost, and regressions.
• Evolve the semantic layer and deliver AI capabilities through APIs with product engineering.

Data and analytics engineering

• Build and maintain reliable ELT pipelines and dbt models in Snowflake using Prefect, Fivetran, and Airbyte.
• Develop trusted data marts and datasets for AI, product analytics, and experimentation.
• Build consistent experimentation metrics, baselines, and feature-impact measurement.
• Maintain data-quality tests, freshness checks, and alerts; diagnose failures and improve pipeline reliability.
• Support warehouse security, governance, performance, and cost management.

Event tracking and product analytics

• Maintain event pipelines from Segment into Amplitude, Iterable, and Snowflake.
• Partner with product engineering to define and validate event-tracking plans for new features.
• Resolve identity, user-property, and event-tracking issues affecting AI and analytics.
• Support product experiments with reliable tracking and measurement, and maintain Amplitude dashboards and reports.

Required qualifications

• 6+ years in software, data, or ML engineering, including 2+ years building and operating production LLM applications.
• Strong Python and SQL skills and experience building reliable production systems.
• Hands-on AI agent and application development using LLM APIs, agent frameworks, tool calling, retrieval, and structured outputs.
• Experience with LLM evaluations, automated graders, regression testing, experimentation, observability, performance, and cost management.
• Experience deploying services on AWS, GCP, or Azure using containers and CI/CD.
• Working knowledge of Snowflake or similar warehouses, dbt, and orchestration tools such as Prefect, Airflow, or Dagster.

Additional relevant experience

• Product analytics and event tracking platforms such as Segment, RudderStack, and Amplitude, or willingness to learn, are noted.
• BI tools such as Sigma, Looker, or Tableau are noted.
• Prompt optimization, semantic layers, data contracts, and A/B testing or experimentation infrastructure are bonuses.

Compensation and benefits

• Salary range is CAD 185,000–220,000; a salary cadence is not specified.
• Benefits include health, dental, vision, life and disability insurance, flexible PTO, wellbeing options, holidays, and an RRSP.

Workplace

• Hybrid working location.

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