Staff AI Engineer, People Technology
U.S.Full-TimeStaff
Salary174986 - 209983 USD per year
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Job Details
- Experience
- 7+ years
- Required Skills
- PythonSQLETLGCPSalesforceCI/CDRESTful APIsWorkdayBigQuery
Requirements
- 7+ years of engineering or data engineering experience
- 2+ years working with AI/ML systems or LLM-based workflows
- Demonstrated ability to set technical direction, mentor engineers, and drive organization-wide impact through AI or automation initiatives
- Strong experience working with Google Cloud Platform
- Strong experience working with BigQuery for data processing and analytics
- Proficiency in Python
- Proficiency in SQL
- Proficiency in modern APIs
- Proficiency in LLM platforms or frameworks (OpenAI, Gemini, Claude, or similar)
- Experience integrating enterprise SaaS systems such as HRIS or CRM platforms
- Experience designing scalable data pipelines
- Experience designing automation frameworks
- Experience designing analytics platforms
- Strong knowledge of data modeling
- Strong knowledge of ETL/ELT pipelines
- Strong knowledge of analytics infrastructure
- Experience working with sensitive data environments
- Experience implementing responsible AI practices, including anonymization, access controls, governance, and regulatory considerations when applying AI to People data
- Ability to partner with both technical and non-technical stakeholders to define and deliver solutions
- Ability to document systems clearly
- Ability to translate evolving business needs into scalable technical solutions
Responsibilities
- Design and build AI-powered workflows, agents, and analytics tools that transform People data into actionable insights and reduce manual processes across the People Team
- Architect solutions that leverage BigQuery as the central data layer, integrating platforms such as Workday, Greenhouse, Docebo, Tangelo, Salesforce, and other internal systems
- Establish and maintain CI/CD pipelines, testing frameworks, and observability standards for AI systems and automated workflows
- Define prompt engineering standards, version control practices, and evaluation frameworks for LLM-based systems operating on People data
- Partner with People Analytics to ensure AI systems operate on well-governed, high-quality datasets and align with established workforce metrics and data models
- Build internal dashboards, AI assistants, or automated workflows that support reporting, insights, and operational efficiency
- Collaborate with People partners to translate business problems into scalable technical solutions
- Define and track success metrics and measurable business outcomes for AI initiatives
- Design systems that securely handle sensitive employee data using anonymization, aggregation, and robust access controls
- Establish governance standards for AI models, prompts, and automation workflows, ensuring compliance with internal security, privacy, and regulatory requirements
- Implement monitoring and evaluation frameworks that ensure AI systems operate accurately, fairly, and reliably over time
- Partner closely with Data Engineering and teams across People, IT, Security and Privacy, Finance, and GTM Operations to align data architecture and AI capabilities
- Collaborate with other AI Engineers across the organization to align on architecture patterns, shared tooling, and company-wide AI standards
- Document systems, architecture decisions, and governance frameworks for People AI initiatives
- Help establish internal standards for AI experimentation, deployment, and measurement of impact
- Provide guidance and enablement to People teams adopting AI-driven tools and workflows
- Create training materials, playbooks, and scalable frameworks that enable People team members to confidently build, trigger, and measure AI-assisted workflows independently
- Set technical direction for AI architecture within the People technology ecosystem
- Mentor and provide technical guidance to engineers and technical partners working on adjacent systems
- Influence cross-functional architecture decisions, advocating for responsible, scalable, and well-governed AI practices across the organization
- Drive alignment between AI initiatives and broader engineering standards, ensuring People systems are not built in isolation
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