AI Engineer / AI Analyst

Light & Wonder•Las Vegas, NV

1d•$100,000 - $120,000•Hybrid

About The Position

Light & Wonder’s corporate team is comprised of incredible talent that works across the enterprise, defying boundaries to provide essential services in an extraordinary manner to ensure the success of the organization and the well-being of employees. Position Summary The Mission: Support the AI transformation program and the COE’s enterprise governance by ensuring AI tools and solutions are evaluated, governed, and scaled in a structured, compliant manner. Job Summary: The AI Governance & Delivery Analyst owns the operational backbone of LNW's enterprise AI program. This is primarily a governance, coordination, and analytics role: you keep the AI intake queue and pipeline moving, keep evaluations and approvals on track, and give the COE and leadership a clear, current picture of adoption, cost, and risk. You act as the operational bridge between the COE engineering team and the business-unit AI Champions, ensuring requests, licensing, access, and vendor evaluations progress cleanly from intake through to delivery. Alongside this, you build, configure, and maintain the tooling and integrations that make the program run: pipeline and usage dashboards, cost and consumption reporting, lightweight automations such as n8n workflows that drive dynamic notifications and token-cap adjustments, and the hands-on configuration that stands enterprise AI platforms up and connects them to the business, including building agentic workflows, wiring up connectors and agents, and configuring MCP servers and tool integrations. A representative example is deploying an enterprise AI assistant such as Glean, configuring its connectors to source systems, and validating permissions and data access. Expect roughly 70% governance, coordination, evaluation, and reporting, and 30% hands-on tooling and configuration. This is not a full-time software engineering role: you configure, integrate, and build internal tools rather than ship production AI services. You will work closely with Architecture, Security, DevOps, Legal, Privacy, and business stakeholders to keep AI initiatives well governed and delivering measurable value.

Requirements

5+ years across governance, risk and compliance, business analysis, data and operations, or program delivery in enterprise environments, ideally in roles that touch technical platforms.
Proven ability to turn ambiguous requests into clear requirements, acceptance criteria, and measurable success metrics for both delivery work and governance artifacts.
Strong operational coordination: managing intake and pipeline workflows, tracking SLAs, and driving items to completion across multiple teams, using Jira, Smartsheet, or equivalent tooling.
Working knowledge of governance and control concepts: data classification, privacy and security review, access controls, change control, and third-party or vendor risk.
Experience coordinating testing and UAT, and comfort building structured evaluation artifacts (test plans, expected behaviours, defect triage, rubric-based LLM evaluation).
Strong documentation discipline and attention to detail, with the ability to produce audit-ready evidence packs, risk-scoring records, and executive-ready reporting.
Comfort with KPI dashboards and analytics, and the ability to turn raw usage and cost data into clear reporting for technical and non-technical audiences.
Working understanding of how GenAI applications are built (agents, tool and function calling, RAG, prompting) sufficient to evaluate, test, and govern them, together with familiarity with enterprise platforms such as Claude/Anthropic, ChatGPT/OpenAI, Microsoft Copilot, Google Gemini, and cloud AI services (AWS Bedrock, Azure AI Foundry).
Working proficiency in Python and/or JavaScript/Node, sufficient to build and maintain internal dashboards, automations, and integrations. This is a tooling requirement, not a production software-engineering one.
Hands-on ability to configure and integrate enterprise AI platforms: building agentic workflows, configuring connectors and agents, and setting up MCP servers and tool integrations. For example, deploying an enterprise AI assistant, configuring its connectors to source systems, and validating permissions and data access. This is configuration and integration work, not full-stack product development.
Excellent stakeholder management, with the ability to drive follow-through across Security, Legal, Architecture, Engineering, and business teams.

Nice To Haves

Familiarity with LLM orchestration frameworks (LangChain/LangGraph, LlamaIndex, Semantic Kernel) and observability or evaluation tooling (Promptfoo, Azure) used to assess and compare AI solutions.
Familiarity with Responsible AI and risk-management frameworks (for example, NIST AI RMF, EU AI Act risk classification, model lifecycle controls).
Exposure to cost governance and FinOps practices for usage-based AI platforms (token cost tracking, consumption attribution, budget alerts, optimization).
Understanding of API gateway patterns and MCP-style tool and connector architectures, including throttling and rate limiting.
Experience with enterprise governance forums (AI Steering Committee, QBRs) and maintaining portfolio health dashboards.
Comfort with Git-based workflows and containerization concepts (Docker) for maintaining internal tooling.
Exposure to GenAI solution development and agentic or multi-agent design, helpful for evaluating what business teams build.

Responsibilities

Help run the AI intake queue and pipeline dashboard (Jira and Smartsheet) within the COE's intake process: progress the requests allocated to you through the evaluation and approval stages, keep their status current, and report SLAs and portfolio health to the COE and leadership.
Coordinate tool onboarding end to end: licensing, access requests, vendor-evaluation scheduling, and tracking of integration and API requests through to delivery.
Serve as the operational interface between the COE infrastructure and engineering team and the business-unit AI Champions network, keeping both sides aligned on priorities and status.
Run structured evaluations of AI tools, platforms, and agents: build test plans, coordinate UAT, apply rubric-based LLM evaluation, and maintain registries, evidence packs, and lifecycle records.
Monitor adoption, cost and consumption, and quality metrics across the AI estate, surfacing risks and anomalies early.
Build, configure, and maintain the supporting tooling and integrations that run the program: pipeline dashboards, usage and cost reporting, lightweight automation (for example, n8n workflows for notifications and token-cap consumption adjustments), and the hands-on configuration of agentic workflows, connectors, agents, and MCP servers that brings enterprise AI platforms online (for example, deploying Glean and wiring its connectors to enterprise source systems). This is roughly a quarter of the role.