• 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, including efficiency gains, time savings, and decision quality improvements • 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.

• 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, including efficiency gains, time savings, and decision quality improvements. • 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.

• Design and implement AI systems that leverage large language models for legal document processing and analysis. • Develop and optimize prompts for effective model interactions in legal contexts. • Fine-tune and adapt models for specialized legal tasks and workflows. • Create robust testing and evaluation frameworks to ensure AI system reliability and accuracy. • Work closely with backend teams to integrate AI capabilities into our existing infrastructure. • Stay current with AI advancements and identify opportunities for applying new technologies.

• Design and oversee the development of scalable generative AI systems and enterprise-grade AI platforms. • Establish robust architectures that support model training, inference, monitoring, and lifecycle management in production environments. • Direct the selection, customization, and enhancement of state-of-the-art generative AI and large language models. • Develop and execute APIs, microservices, and integration frameworks to incorporate AI capabilities into enterprise applications. • Ensure that AI platforms meet stringent standards for performance, reliability, security, and scalability, while also adhering to data governance and privacy regulations. • Collaborate closely with product, engineering, and business teams to outline technical requirements and approaches to AI architecture. • Architect end-to-end pipelines for deploying and monitoring AI models, ensuring seamless integration with existing systems. • Guide architectural decisions for LLM applications, AI workflows, and distributed AI infrastructure. • Institute best practices for ethical AI development, including strategies to mitigate risks like model hallucinations, bias, and reliability challenges. • Provide technical mentorship and guidance to engineering teams, while contributing to the formulation of long-term technology strategies and the advancement of AI platforms.