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.

Essential Job Functions:

• 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.

Outcomes:

• Secure, scalable, production-ready AI services and integrations, delivered to enterprise standards and operated reliably in production.

• A robust, reusable MCP platform (servers, gateways, guardrails, and agent components) that lets the business build and integrate AI capabilities safely and at pace.

• A trusted, reusable, and well-governed AI foundation that enables innovation at pace while maintaining the operational discipline and regulatory integrity required by LNW’s enterprise governance framework.

These attributes are required unless otherwise specified as preferred.

Required

• 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.

Preferred

• 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.

The targeted pay range for this role is $100,000-$120,000. The total compensation package for this position may also include applicable incentive compensation, such as an annual performance bonus. Actual compensation packages are based on several factors that may include, but are not limited to skill set, depth of experience, specific work geography, as well as internal equity and alignment with market data.

Physical Requirements:

The physical demands described here are representative of those that must be met by an individual to successfully perform the essential functions of this job. Reasonable accommodation may be made to enable individuals with disabilities to perform these functions. While performing the duties of this job, the employee is regularly required to sit, stand, walk, bend, use hands, operate a computer, and possess specific vision abilities, including close and distance vision and the ability to adjust focus while working with computers and business equipment.

Location:
​Austin, TX strongly preferred; Las Vegas, NV will also be considered. This is a hybrid position requiring onsite presence four days per week.

Work Conditions:

This role is performed in a collaborative, fast-paced environment and may be based in a hybrid work setting, depending on business needs. The position requires regular partnership with cross-functional teams and may involve occasional flexibility in working hours to support priorities across time zones. Travel is expected to be minimal, up to 5%. Work is primarily performed in an office or remote setting with standard computer and communication tools, in compliance with company safety, security, and policy requirements.

This job description should not be interpreted as all-inclusive; it is intended to identify major responsibilities and requirements of the job. The employee in this position may be requested to perform other job-related tasks and responsibilities than those stated above.

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