The opportunity
Reporting to the Senior Manager, People Strategy Operations, the Senior AI Enablement Engineer, People, is a technical founder embedded within the People function.
What you'll do
Building the Core AI & Automation Layer (~50%) Drive the technical execution: of the People team's AI transformation roadmap—architecting, building, and maintaining intelligent solutions around our prioritized operational workflows.
Build agentic workflows, RAG pipelines, and system integrations required to: scale workflows from ‘Human + AI handoffs’ to ‘AI as a system’ level maturity.
Scan externally for emerging AI capabilities and market patterns; translate: signal into written briefs that inform what the team experiments on next.
Embed long-term operational success by creating reproducible blueprints,: demos, and guides that naturally transfer expertise and cultivate sustainable AI ownership across the People team.
Data Layer & AI Reliability (~25%) Partner with the People Analyst and: Senior Manager of Data Management & Governance to deeply understand the existing Snowflake data model to identify what is trusted, what is fragile, and what the AI layer can reliably consume.
Assess the custom Workday-to-Snowflake pipeline to understand its: architecture and engineering limitations, ensuring any AI tools built on top of it are designed to handle data quirks reliably.
What they're looking for
- + years of professional, hands-on builder/software engineering experience.
- + years of hands-on experience building with LLMs and AI tooling in: production: agent frameworks, pipelines, retrieval systems, or AI-integrated workflows that real users depend on.
- Familiarity with LLM observability tooling (LangSmith, Datadog LLM: monitoring, or equivalent): you instrument what you build and use signal to improve it
- Systems-level understanding of data retrieval with comfort working with: vector embeddings and combining structured data, such as Snowflake tables, with unstructured text to power intelligent, context-aware internal tools.
- Strong working knowledge of data privacy standards, including experience: building audit trails, role-based permissions, and robust access controls for highly confidential datasets.
- Familiarity with HR/People systems such as Workday or Greenhouse is a meaningful plus, but not required.
- Strong communication skills: able to present technical options, trade-offs, and progress to non-technical People and business stakeholders.
- Demonstrated ability to work independently in ambiguous, greenfield: environments: defining scope, sequencing work, and driving to production without heavy oversight.
