AI & Data Engineer, HR Systems

Riot's Enterprise Technology organization ensures Rioters have what they need to unlock their full potential, from secure and compliant systems to efficient business platforms that keep the company running smoothly. HR Tech & Systems builds and evolves the technology ecosystem that supports Rioters across the employee lifecycle.

As a Staff AI & Data Engineer, HR Systems , you will bring deep AI and modern data engineering expertise to HR Systems, accelerating our evolution into an AI-augmented and data-enabled engineering organization. You will be a hands-on builder who designs and delivers AI agents, intelligent automations, data products, and reusable engineering capabilities across Riot's HR Technology ecosystem.

You will combine strong AI and data engineering expertise with meaningful HR technology and domain knowledge. You will work across Workday, Databricks, and Riot's broader AI and enterprise technology ecosystem to turn trusted HR data and complex HR processes into secure, scalable solutions.

Just as importantly, you will help build these capabilities within the existing team. You will pair with HR Tech engineers, establish practical engineering patterns, and provide hands-on mentorship in AI, Databricks, and modern development practices so the team can increasingly design, build, and support these solutions independently.

You will partner closely with HR Tech & Systems, Enterprise Data & AI, Enterprise Software Engineering, HR COEs, and other Enterprise Technology teams.

The role reports to the Senior Manager, HR Tech & Systems.

• Apply deep HR technology knowledge to discover opportunities and develop solutions across HR operations, recruiting, onboarding, compensation, benefits, learning, talent, workforce planning, and HR analytics.
• Leverage Riot's evolving AI and automation ecosystem, including tools such as Workato, Windsurf, Devin, and True Foundry, to accelerate development and delivery.
• Evaluate opportunities within HR Technology's workflow and portfolio of applications, including SaaS and custom systems, to establish best practices and introduce new AI-enhanced capabilities.
• Partner with HR stakeholders, product owners, and technical teams to identify high-value opportunities and determine when AI, automation, data products, integrations, or native platform capabilities are the most appropriate solution.
• Translate complex HR processes, policies, and operational needs into scalable technical solutions that improve Rioter experiences and business outcomes.
• Drive solutions through the full lifecycle from discovery and prototyping through implementation, adoption, measurement, and continuous improvement.

• Design and build agentic AI solutions using LLMs, RAG, tool calling, memory, orchestration, and APIs to make business-context-aware decisions and take actions within defined guardrails.
• Leverage Databricks AI capabilities-including Unity Catalog, model serving, and vector search-and select appropriate models, prompting, retrieval, and orchestration patterns based on business needs, performance, scalability, and cost.
• Establish evaluation, testing, and observability practices that ensure AI solutions are accurate, grounded, reliable, safe, measurable, and production-ready.
• Design secure and responsible AI solutions with appropriate identity-aware access, human oversight, transparency, auditability, and controls for sensitive HR data and workflows.
• Partner with Enterprise Data & AI and Security to establish reusable AI patterns, standards, and guardrails, rapidly evaluating emerging capabilities and scaling those that demonstrate measurable business value.

• Design scalable HR data pipelines, products, and domain models on Databricks using medallion architecture and Unity Catalog to provide governed, secure access to trusted HR data.
• Integrate structured and unstructured data across Workday, Databricks, enterprise applications, and AI services using MCPs, APIs, events, pipelines, and modern orchestration patterns.
• Establish data products, contracts, semantic models, and metadata that define ownership, business meaning, quality, lineage, freshness, and appropriate use of HR data.
• Ensure HR data is fit for analytics and AI through clear source-of-truth definitions, standardized business concepts, and strong data quality, privacy, security, and governance practices.
• Design resilient architectures that appropriately balance…