Job description

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. The role will report to the Manager of Enterprise Systems Engineering. 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. Required Qualifications: 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. Desired Qualifications: 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. For this role, you'll find success through craft expertise, a collaborative spirit, and decision-making that prioritizes your fellow Rioters, who are the customers of your work. Being a dedicated fan of games is not necessary for this position! Our Perks: Riot focuses on work/life balance, shown by our open paid time off policy and other perks such as flexible work schedules. We offer medical, dental, and life insurance, parental leave for you, your spouse/domestic partner, and children, and a 401k with company match. Check out our benefits pages for more information. At Riot Games, we put players first . That mission drives every decision in our quest to create games and experiences that make it better to be a player. Whether you’re working directly on a new player-facing experience or you’re supporting the company as a whole, everyone at Riot is part of our mission. And just like in our games, we’re better when we work together. Our goal is to create collaborative teams where you are empowered to bring your unique perspective everyday. If that sounds like the kind of place you want to work, we’re looking forward to your application. It’s our policy to provide equal employment opportunity for all applicants and members of Riot Games, Inc. Riot Games makes reasonable accommodations for handicapped and disabled Rioters and does not unlawfully discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, handicap, veteran status, marital status, criminal history, or any other category protected by applicable federal and state law. We consider for employment all qualified applicants, including those with criminal histories, in a manner consistent with applicable federal, state and local law, including the California Fair Chance Act, the City of Los Angeles Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, the San Francisco Fair Chance Ordinance, and the Washington Fair Chance Act. Per the Los Angeles County Fair Chance Ordinance, the following core duties may create a basis for disqualifying candidates with relevant criminal histories: Safeguarding confidential and sensitive Company data Communication with others, including Rioters and third parties such as vendors, and/or players, including minors Accessing Company assets, secure digital systems, and networks Ensuring a safe interactive environment for players and other Rioters These duties are directly related to essential operations, safety, trust, and compliance obligations within our organization. Please note that job duties may evolve based on business needs and additional responsibilities may be assigned as necessary to maintain operational efficiency and security.

Requirements

Must-haves

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.
The role will report to the Manager of Enterprise Systems Engineering.
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.
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.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aici/cdllmtypescriptreactnextjsgraphqlawsdockerkubernetespython

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Dental

401k

Paid Time Off

Parental Leave