Staff Application Engineer - Enterprise Data & AI (Intelligence Platform)
Riot Games · Los Angeles, USA
About this role
**About the team** Riot’s Enterprise Technology organization ensures Rioters have what they need to unlock their full potential—from efficient business platforms to next-generation AI capabilities. As a **Staff Data and AI Engineer** on the **Enterprise Data & AI** team, you’ll build **governed data foundations** that power enterprise analytics, automation, and applied AI. **What you’ll do** - Design and operate **production data pipelines** in **Databricks** using a **medallion architecture** (bronze/silver/gold) - Integrate and curate data into **semantic and domain models** using **Unity Catalog** (with subject-matter experts) - Build **agents** and **retrieval-augmented applications** on top of governed data using Databricks capabilities (e.g., **Unity Catalog functions**, **Mosaic AI**, **model serving**) - Own systems end-to-end—from architecture through **production operations**—with strong practices in **engineering, data quality, security, evaluation, and observability** --- ## Responsibilities ### Data Pipelines and Semantic Layer (Primary) - Build and operate pipelines ingesting source-system data into Databricks via medallion architecture, including **lineage, provenance, access controls, and logging** - Partner with subject-matter experts to define and evolve **domain/ontology/semantic models** in Unity Catalog, and curate reliable data products into those models - Own **data quality** with tests, monitoring, and alerting so issues surface before downstream consumers - Integrate enterprise systems through **APIs, events, and middleware**, applying authentication, retries, reconciliation, and auditability ### Agents on the Databricks Data Layer (Secondary) - Build production agents and retrieval-augmented applications grounded in governed Databricks data, using Unity Catalog functions as tools and Mosaic AI/model-serving capabilities - Add **authorization, approvals, and auditability** for consequential actions performed through agents or AI-enabled orchestration - Create **evaluation sets** and **observability** for agents (groundedness, latency, cost, production feedback) and use results to improve them ### Engineering Fundamentals - Apply solid engineering practices: automated testing, code review, CI/CD, documentation, and on-call support - Own architecture decisions and clearly communicate tradeoffs to technical and business partners --- ## Required Qualifications - Bachelor’s degree in a related field, or equivalent practical experience - **8+ years** experience in data engineering, software/application engineering, or adjacent enterprise technology - Hands-on experience building **production data pipelines** (ingestion, semantic/domain modeling, data quality, lineage) - Hands-on **Databricks** experience: **Unity Catalog**, medallion architecture, and **Mosaic AI** or **model serving** - Hands-on experience shipping production AI solutions (e.g., retrieval-augmented, conversational, or agentic), grounded in data pipelines you built - Proficiency in **Python** and strong software engineering fundamentals (testing, code review, CI/CD, deployment, production support) - Experience integrating enterprise systems via APIs/events/middleware (authentication, retries, reconciliation, auditability) - Experience with an LLM/cloud AI platform (e.g., **Vertex AI, Anthropic, OpenAI**) and an automation platform (e.g., **Workato, n8n, Camunda**, or similar) - Working knowledge of **data governance, privacy, and security** pr
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