AI Engineer

Light & Wonder•Las Vegas, NV

21h•$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 Engineer builds and operates the technical foundation of LNW’s enterprise AI program. This is a hands-on software and platform engineering role: you build, integrate, and run the AI services, integrations, and platform capabilities the rest of the program depends on. You take AI initiatives from technical evaluation through to production: standing up agentic workflows and multi-agent systems, building backend services and APIs, integrating AI capabilities into enterprise systems, and building out the MCP platform, including its servers, gateways, guardrails, permissions, and agent workflows. You are the engineering counterpart to the AI Governance & Delivery Analyst: the Analyst runs intake, governance, evaluation coordination, and reporting, while you own the technical build. Architecture and solution design sit with the team’s Architect: you build and operate against those designs. You conduct deep technical assessments of emerging AI tools, platforms, and agents, prove out solutions through proofs of concept, and turn approved use cases into secure, scalable, production-ready services. Working closely with Architecture, Security, DevOps, and business stakeholders, you ensure AI initiatives are technically sound, well engineered, properly secured, and delivering measurable business value. This is a production engineering role: you ship and operate enterprise AI services rather than only configuring them.

Requirements

5+ years of professional software engineering experience, with a strong recent focus on GenAI or AI/ML application development in enterprise environments.
Strong hands-on software development in Python, and Java and/or TypeScript/Node.js, with a proven track record building backend services, APIs, and integrations with enterprise systems (authentication, authorization, logging, monitoring).
Hands-on GenAI/LLM application engineering: agents, tool and function calling, RAG architectures, embeddings, vector search, and prompt engineering.
Proven experience building GenAI solutions and virtual agent orchestration: agentic workflows, multi-agent systems, conversational AI, and AI-assisted automation for enterprise processes.
Hands-on experience with enterprise GenAI platforms and foundation models (ChatGPT/OpenAI, Claude/Anthropic, Microsoft Copilot, Google Gemini) and cloud AI services such as AWS Bedrock, Azure AI Foundry, or equivalent multi-model environments.
Hands-on experience building and integrating API and MCP-style tool and connector services, including MCP servers and gateways, guardrails, permissions, and multi-tenant patterns with throttling and rate limiting.
Solid infrastructure and DevOps fundamentals: Git-based workflows, CI/CD, containerization (Docker), infrastructure as code, and cloud deployment patterns, with the ability to stand up and operate services in production.
Strong security mindset: secrets management, encryption, audit logging, and secure API design, with familiarity with LLM-specific threats and mitigations.
Demonstrated ability to turn ambiguous requirements into working, well-documented technical solutions with clear acceptance criteria and measurable outcomes.
Comfort building structured technical evaluation artifacts: test plans, expected behaviours, defect triage, and rubric-based LLM evaluation.
Excellent collaboration and communication, with the ability to drive technical follow-through across Security, Architecture, Engineering, and business teams.

Nice To Haves

Experience with LLM orchestration frameworks (LangChain/LangGraph, LlamaIndex, Semantic Kernel) and observability or evaluation tooling (Promptfoo, Azure, or Grafana/Loki-style stacks).
Experience integrating enterprise identity and access (Okta, Microsoft Entra) and implementing secure SSO and OAuth patterns for AI services.
Experience with Kubernetes and infrastructure as code (Terraform, CloudFormation), plus cloud networking and reverse-proxy patterns (for example, nginx).
Familiarity with Responsible AI and risk-management frameworks (NIST AI RMF, EU AI Act risk classification, model lifecycle controls).
Experience with output-quality measurement, resiliency checks, and human-in-the-loop review processes for GenAI/LLM systems.
Exposure to cost governance and FinOps practices for usage-based AI platforms (token cost tracking, consumption attribution, budget alerts, optimization) and cost-aware architecture.
Experience with enterprise governance forums (AI Steering Committee, QBRs) and building KPI and observability dashboards.
Regulated-industry experience (gaming, financial services, healthcare) and awareness of the associated compliance controls.

Responsibilities

Build and operate production AI services and integrations: agentic workflows, multi-agent systems, RAG pipelines, and tool and function-calling integrations wired into enterprise systems.
Build out the enterprise MCP platform: MCP servers and gateways, tool and connector integrations, guardrails, permissions and access boundaries, and reusable agent components.
Develop backend services and APIs in Python, and Java and/or TypeScript/Node.js, with enterprise-grade authentication, authorization, logging, monitoring, and observability.
Deploy and operate AI workloads on cloud AI platforms including AWS Bedrock (with AgentCore), Azure AI Foundry, and equivalent multi-model environments, infrastructure best practices, CI/CD, and containerization practices.
Conduct deep technical evaluations of emerging AI tools, platforms, models, and agents, run proofs of concept, and build the evaluation harnesses and rubric-based LLM test tooling that feed the COE’s governance and approval process.
Engineer security and reliability into every solution: secrets management, encryption, secure API design, audit logging, guardrails, and mitigations for LLM-specific threats, building for resiliency, quality, and cost-aware operation.
Partner with the AI Governance & Delivery Analyst and with Architecture, Security, DevOps, and business stakeholders to move initiatives from intake through to production cleanly and compliantly.