Staff Application Engineer - Enterprise Data & AI (Intelligence Platform)

Riot Games; Los Angeles (CA); On-site; USD 180,000 - 260,000; Full time

Job summary

Riot Games is seeking a Staff Data and AI Engineer to design and operate governed data pipelines in Databricks, defining semantic models and secure data products. You will build production AI applications and agents leveraging Mosaic AI and Unity Catalog, collaborating with SMEs to evolve domain models.

You will own architecture, data quality, and observability, delivering reliable data products while applying strong engineering practices and ensuring auditable, secure systems.

Qualifications

• Hands-on experience building production data pipelines, including ingestion, semantic modeling, data quality, and lineage.
• Hands-on Databricks experience, including Unity Catalog, medallion architecture, Mosaic AI or model serving.
• Experience shipping production AI solutions, such as retrieval-augmented, conversational, or agentic applications.

Responsibilities

• Data Pipelines and Semantic Layer (Primary) – build and operate pipelines ingesting source data into Databricks in a medallion architecture with lineage and access controls.
• Define domain and semantic models in Unity Catalog with SMEs; curate reliable data products.
• Own data quality, testing, monitoring, and alerting for pipelines and products.

Skills

Python

Databricks

CI/CD

Data Pipelines

Model Serving

Security & Governance

APIs & Integration

ML Platforms

Education

Bachelor's degree

Tools

Unity Catalog

Mosaic AI

LangGraph

LangChain

Job description

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 will build the governed data foundations that power enterprise analytics, automation, and applied AI. Your primary focus will be data architecture and data engineering, where you will be responsible for designing and operating production data pipelines in Databricks, integrating & curating data through a medallion architecture, and defining semantic and domain models in Unity Catalog with subject-matter experts.

You will also build agents and retrieval-augmented applications directly on this governed data using Databricks capabilities such as Unity Catalog functions, Mosaic AI, and model serving. You will own your systems from architecture through production operations, applying strong engineering, data quality, security, evaluation, and observability practices throughout

The ideal candidate is a hands‑on data and software engineer who can turn ambiguous enterprise needs into reliable data products and focused AI-enabled solutions.

The role will report to the Senior Manager, Enterprise Data and AI.

Responsibilities:

• Data Pipelines and Semantic Layer (Primary)
• Build and operate pipelines that ingest source-system data into Databricks using a medallion architecture across bronze, silver, and gold layers, with lineage, provenance, access controls, and logging.
• Partner with subject-matter experts to define and evolve domain, ontology, and semantic models in Unity Catalog, then curate reliable data products into those models.
• Own data quality for the pipelines and products you build, implementing tests, monitoring, and alerting so issues surface before they reach downstream consumers.
• Integrate enterprise systems through APIs, events, and middleware while applying appropriate 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 or model-serving capabilities.
• Add authorization, approvals, and auditability for consequential actions performed through agents or AI-enabled orchestrations.
• Create evaluation sets and observability for the agents you ship, including groundedness, latency, cost, and production feedback, and use the results to improve them.
• Engineering Fundamentals
• Apply solid engineering practices, including automated testing, code review, CI/CD, documentation, on-call support, and concise design notes.
• Own architecture decisions for your systems and communicate material tradeoffs clearly to technical and business partners.

Required Qualifications:

• Bachelor's degree in a related field, or equivalent practical experience.
• 8+ years of experience in data engineering, software or application engineering, or an adjacent enterprise technology role.
• Hands‑on experience building production data pipelines, including ingestion, semantic or domain modeling, data quality, and lineage.
• Hands‑on Databricks experience, including Unity Catalog, medallion architecture, and Mosaic AI or model serving.
• Hands‑on experience shipping production AI solutions, such as retrieval-augmented, conversational, or agentic applications, grounded in data pipelines you built.
• Proficiency in Python and strong software engineering fundamentals, including testing, code review, CI/CD, deployment, and production support.
• Experience integrating enterprise systems through APIs, events, or middleware, including authentication, retries, reconciliation, and auditability.
• Experience with an LLM or cloud AI platform such as Vertex AI, Anthropic, or OpenAI and an automation platform such as Workato, n8n, Camunda, or similar.
• Working knowledge of data governance, privacy, and security practices applied to production data pipelines and products.

Desired Qualifications:

• Experience with the Mosaic AI Agent Framework or Unity Catalog functions used as agent tools.
• Experience with enterprise or federated search, vector databases, or permission‑aware retrieval.
• Experience with agent frameworks such as LangGraph, Pydantic AI, LangChain, or CrewAI.
• Experience with LLM or agent evaluation and observability, such as MLflow tracing.
• Experience with dimensional modeling or master data management.
• Experience integrating Workday, ServiceNow, Coupa, Concur, Oracle, OneStream, or Ironclad.
• Experience with GCP or AWS data services, dbt, containers, or serverless computing.

Our Perks: