Staff Software Engineer, Enterprise AI

Riot Games•Los Angeles, CA

2d•$192,500 - $269,400

About The Position

Riot’s Enterprise Technology organization ensures Rioters have what they need to unlock their full potential by building secure, reliable, and scalable internal systems that keep the company running smoothly. As a Staff Full-Stack Software Engineer specializing in AI, you will personally design and build the AI agent capabilities and product experiences for an internal enterprise intelligence platform. This includes the retrieval, orchestration, and reasoning layer that sits on top of a semantic data layer built and maintained by a partner data engineering team. You’ll turn these capabilities into search, chat, and workflow experiences that help leaders find information and make decisions faster. This is a hands-on, coding-majority role. You’ll build the agents, prompts, tool integrations, and full-stack product surface yourself. You’ll work directly with product owners, system leads, and domain experts across HR, Finance, IT, Legal, and Workplace to understand real workflows. You’ll also work closely with a partner data engineering team that owns ingestion, the semantic layer, and the underlying data model. You will consume and help shape requirements for that layer rather than building it yourself. Your work will directly improve how Rioters hire, plan, spend, onboard, and operate at scale. The ideal candidate is a deeply experienced full-stack software engineer who has personally shipped AI-enabled products at scale, is comfortable building in ambiguous and evolving problem spaces, and takes ownership of systems from the first commit through production operation.

Requirements

Bachelor's degree in Computer Engineering, Computer Science, Information Systems, or related field (or equivalent professional experience delivering enterprise technology solutions).
8+ years of professional experience in full-stack software development
Practical, end-to-end experience building RAG or agentic systems: retrieval, tool-calling, structured outputs, multi-step reasoning, and verification/evaluation loops, not just familiarity with LLM APIs.
Experience designing and running evaluation approaches for nondeterministic systems: curated datasets, automated metrics, human review, and production feedback.
Experience establishing basic AI observability (quality, safety, reliability, latency, token usage, cost) for features you build.
Working understanding of AI security and responsible-use risks, prompt injection, tool abuse, data leakage, access control, model limitations, sufficient to build safeguards into what you ship.
Deep hands-on programming experience in Node, TypeScript, React.
Experience with modern frameworks (e.g., NextJS, NestJS).
Experience building RESTful and GraphQL APIs, working with relational (e.g., PostgreSQL, MySQL) and non-relational (e.g., Redis) databases.
Comfortable with cloud infrastructure and deployment: AWS, containers (Docker), orchestration (Kubernetes), CI/CD pipelines.
Familiar with software engineering best practices: automated testing, code review, monitoring/observability, security and performance considerations.

Nice To Haves

Deep practical knowledge of retrieval-augmented generation and semantic search, including embedding and reranking models, retrieval design, grounding, citations, and evaluation.
Experience with agent platforms (e.g., LangGraph, AutoGen, CrewAI, or comparable), vector databases, and multi-agent orchestration patterns.
Working familiarity with Python and lakehouse/data-catalog concepts (e.g., Databricks, Unity Catalog), enough to collaborate effectively with the data engineering team, without needing to own pipeline build-out.
Experience shipping an internal AI product from scratch as part of a small, fast-moving team, comfortable with a "scrappy MVP, iterate" delivery style rather than a fully-specified roadmap.

Responsibilities

Build and maintain an orchestrator + specialist-agent architecture (retrieval, tool use, structured outputs, multi-step reasoning) on top of a semantic data layer owned by a partner data team.
Design and implement retrieval and grounding for RAG/semantic search use cases: embeddings, reranking, citation of evidence, and techniques to reduce hallucination.
Integrate agents with internal and external tools/APIs (function/tool calling) to let them take real actions, not just answer questions.
Build the full-stack application layer, APIs, and UI that exposes AI-powered search, chat, and workflow experiences to end users.
Instrument AI features with observability for quality, latency, token usage, and cost, and use it to drive iteration and tuning.
Iterate on prompts and model choices quickly, with sensible fallback behavior when a model, tool, or retrieval step fails or returns low-confidence output.
Apply solid engineering fundamentals: source control, code review, automated testing (unit, integration, functional), CI/CD, and on-call for the systems you own.