New opportunity

Lead Engineer, AI Development Experience

Live Nation Entertainment · Work From Home - Georgia; Work From Home - Michigan; Work From Home - Tennessee; Work From Home - Florida

About this role

Live Nation Core Data Services seeks a Lead Engineer, AI Development Experience to architect and build foundational tooling that helps technical and non-technical teams create, deploy, and manage AI agents, workflows, applications, and automations. The remote role is listed for Georgia, Michigan, Tennessee, and Florida. The engineer will work with teams across global time zones, including odd hours when necessary.

Responsibilities

Build scalable APIs, SDKs, CLI tools, and workflows that make AI capabilities accessible across the organization. Establish production-grade standards for reliability, observability, security, and operations.
Coach cross-functional teams adopting AI-augmented processes. Produce self-service documentation, best practices, and enablement materials, and develop measures of AI adoption, effectiveness, and return on investment.
Deliver against a roadmap and backlog while managing concurrent initiatives. Partner with Architecture, Data Engineering, Security, and other engineering teams on deployment and maintenance; evaluate third-party solutions and potential partnerships.

Qualifications

8+ years of engineering experience, including architecting and building scalable platforms and developer tools, plus 2+ years of hands-on experience building generative AI tooling involving LLM APIs, prompt engineering, agent frameworks, and AI orchestration platforms.
Principal-level engineering judgment; deep data engineering and architecture knowledge, including RDBMS, Spark or Flink, Kafka, Lucene or Elasticsearch, and Data Mesh implementation. Experience with Databricks and with observability, monitoring, security, and reliability practices for workflows and persistent data.
Ability to document tools and data products for non-technical self-service users, communicate across business functions, coach teams through technology adoption, and track multiple projects in Jira or another agile work management tool. Enterprise cross-functional experience is required. Full-stack application development, prior data engineering, BI, analysis, or full-stack engineering experience, and an interest in live music and events are pluses.

Benefits listed include medical, vision, dental, and mental health coverage; paid time off and concert tickets; a 401(k) match; family-support programs; tuition and student-loan assistance; and volunteer time off.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Generative AILLM APIsPrompt EngineeringAI Agent FrameworksAI OrchestrationAPI DevelopmentSDK DevelopmentCLI DevelopmentPlatform ArchitectureData EngineeringRDBMSApache SparkApache FlinkApache KafkaApache LuceneElasticsearchData MeshDatabricksObservabilityMonitoringSecurity EngineeringReliability EngineeringTechnical DocumentationJiraTechnical CoachingCross-functional Collaboration

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Build accessible AI platform tooling
The role is to architect and build scalable APIs, SDKs, CLIs, and workflows that let technical and non-technical teams create, deploy, and manage AI agents, applications, and automations.
Make AI implementations production-ready
The posting calls for standards covering reliability, observability, security, and operations, supported by hands-on generative AI tooling experience and data-platform engineering judgment.
Ground tooling in data architecture
The role calls for deep knowledge of RDBMS, Spark or Flink, Kafka, Elasticsearch, Data Mesh, and Databricks, including practices for workflows and persistent data.
Enable adoption and measure results
The engineer will coach cross-functional teams, publish self-service documentation and best practices, and build feedback loops measuring AI adoption, effectiveness, and ROI.
Deliver through enterprise partnerships
The role requires progress against a roadmap and backlog across concurrent initiatives, coordination with Architecture, Data Engineering, and Security on deployment and maintenance, and evaluation of third-party solutions.

Likely challenges to think through

Self-service versus operational control
Preparation hypothesis: making AI tooling easy for non-technical users may conflict with production reliability and security. Prepare to explain how an approachable workflow could retain clear safeguards, observability, and maintenance ownership.
Innovation versus architectural fit
Preparation hypothesis: rapidly evolving LLM and agent tooling may be difficult to reconcile with established data systems and persistent workflows. Prepare to discuss how to evaluate a new capability or vendor against integration needs, operational risk, and a pragmatic build-versus-buy decision.
Broad adoption versus measurable value
Preparation hypothesis: usage alone may not show whether AI-augmented work is effective. Prepare a way to distinguish adoption from effectiveness and ROI, using feedback to refine documentation and enablement.
Concurrent delivery across time zones
Preparation hypothesis: multiple initiatives and globally distributed partners may make priorities and handoffs harder to keep clear. Prepare an example of how to make trade-offs visible in a backlog, document decisions, and coordinate deployments across teams.

Practice your answers

Technical practice

How would you design an AI tooling platform that serves both developers and non-technical teams?

What to coverPreparation suggestion: Explain what belongs in shared APIs, SDKs, CLI tools, and workflows, and how each interface supports creating, deploying, or managing AI capabilities. Discuss scalability and the trade-off between flexibility for engineers and simplicity for self-service users.

How would you make LLM APIs and prompts dependable for organization-wide use?

What to coverPreparation suggestion: Describe how you would manage prompt changes, test output quality, and handle failures or variability in LLM responses. Explain the trade-offs between reusable platform conventions and teams’ ability to tailor their applications.

When would you use an agent framework or orchestration platform rather than a simpler workflow?

What to coverPreparation suggestion: Compare the coordination needs of agents, applications, and automations with the added complexity of orchestration. Explain how you would evaluate whether a design remains understandable and maintainable in production.

How would you troubleshoot a production AI workflow that has become unreliable?

What to coverPreparation suggestion: Walk through the signals you would examine, how you would isolate a failure, and what operational safeguards you would add afterward. Relate your approach to the posting’s emphasis on reliability, observability, and operational excellence.

What security standards would you establish for shared AI tooling?

What to coverPreparation suggestion: Discuss access to APIs and persistent data, protection of sensitive information, and how teams could verify that controls work. Explain how you would partner with Security without making self-service use impractical.

How would you choose among RDBMS, Spark or Flink, Kafka, and Lucene or Elasticsearch in a data architecture supporting AI tooling?

What to coverPreparation suggestion: Distinguish storage, processing, event transport, and search needs rather than treating these technologies as interchangeable. Explain how ownership and access to data products could fit a Data Mesh approach, while making clear that no specific architecture is prescribed in the posting.

How would you operate Databricks workflows and persistent data reliably?

What to coverPreparation suggestion: Cover monitoring, failure recovery, security, and the evidence you would use to assess reliability. Explain how operational responsibilities would be shared with Data Engineering and other engineering teams.

How would you document an AI tool for non-technical self-service users?

What to coverPreparation suggestion: Describe the essential setup, usage, limitations, and troubleshooting guidance you would provide. Explain how you would test whether the material reduces support needs and time-to-value.

How would you measure AI adoption, effectiveness, and return on investment?

What to coverPreparation suggestion: Propose distinct measures for usage, outcomes, and costs, plus a feedback loop for improving the tooling. Discuss how you would avoid mistaking high usage for meaningful business value.

How would you evaluate a third-party AI solution against building platform capability internally?

What to coverPreparation suggestion: Compare fit with user needs, integration and maintenance effort, security, reliability, and cost. Explain what evidence you would seek before recommending a partnership or an internal build.

Behavioural practice

Tell me about a time you coached a cross-functional team through adopting a new technical workflow.

What to coverPreparation suggestion: Give the situation, your specific teaching and enablement actions, and the result for the team. Explain how you adjusted your approach for people with different technical backgrounds.

Describe a time you built agreement among Architecture, Data Engineering, Security, or other stakeholders with competing priorities.

What to coverPreparation suggestion: Set out the disagreement, how you gathered evidence and handled objections, and the resulting decision. Make your own contribution and any remaining trade-offs clear.

Tell me about a time you managed several engineering initiatives while delivering against a roadmap.

What to coverPreparation suggestion: Explain the situation, how you prioritized and tracked work, and what you communicated when priorities changed. Give the outcome, including any commitments you revised rather than implying everything went to plan.

Describe a time you coordinated work across time zones to resolve an engineering dependency or issue.

What to coverPreparation suggestion: Describe the situation, your communication and handoff actions, and the result. Explain how you balanced timely collaboration with clear ownership across teams.

Tell me about a time you made an engineering decision despite significant ambiguity.

What to coverPreparation suggestion: State what was unknown, the evidence you gathered, the decision you made, and the result. Explain how you balanced speed, pragmatism, and production requirements, including what you changed after learning more.

Sharpen your knowledge

Topics to prepare

AI developer-platform architecture
Suggested exercise: Sketch how APIs, SDKs, CLI tools, and workflows could provide a consistent way for technical and non-technical teams to create and manage AI applications. Explain scalability and interface trade-offs. The posting calls for foundational, organization-wide AI tooling.
Generative AI orchestration
Suggested exercise: Diagram an agent workflow using an LLM API, prompts, an agent framework, and an orchestration platform; identify where failures or unintended actions could occur. The posting explicitly seeks hands-on generative AI tooling experience.
Data architecture and Data Mesh
Suggested exercise: Explain when an RDBMS, Spark or Flink, Kafka, and Lucene or Elasticsearch would serve different parts of a data flow, then discuss how Data Mesh changes ownership. The posting names these technologies and Data Mesh implementation.
Production standards for AI and data workflows
Suggested exercise: Review a hypothetical Databricks workflow with persistent data and state what reliability, monitoring, observability, and security standards you would apply. The posting requires Databricks experience and production-grade operational judgment.
Enterprise adoption and measurement
Suggested exercise: Propose definitions for adoption, effectiveness, time-to-value, and ROI, including what feedback would change the tooling roadmap. The role includes coaching teams and building organization-wide measurement frameworks.

Skills to revise

Build a usable developer-tool interface
Suggested exercise: Draft an API contract and a matching SDK or CLI interaction for creating and managing an AI workflow; include errors and a simple non-technical usage path. The role builds scalable APIs, SDKs, CLIs, and workflows.
Prototype and assess an agent workflow
Suggested exercise: Build a small workflow using an LLM API and prompts, then test how the agent framework or orchestration handles unsuccessful steps. The posting asks for hands-on generative AI tooling, not just familiarity with its concepts.
Write self-service guidance
Suggested exercise: Write a short setup guide, worked example, and troubleshooting section for a non-technical user of the proposed tool. The role explicitly owns documentation, best practices, and enablement materials.
Coach a cross-functional rollout
Suggested exercise: Rehearse a brief walkthrough for business, Data Engineering, and Security stakeholders; explain the workflow, invite objections, and identify decisions needing their input. The posting emphasizes coaching and collaboration across functions.
Prioritize and track concurrent work
Suggested exercise: Turn three competing tooling requests into a prioritized backlog with dependencies, ownership, and progress updates in Jira or another agile work management tool. The posting expects delivery against a roadmap while tracking multiple initiatives.

Your preparation checklist

Prepare a developer-tooling project walkthrough
Choose a real example, if available, and explain your architecture, contributions, scale, and results. Highlight APIs, SDKs, CLIs, or workflows, plus hands-on LLM API, prompt, agent, or orchestration decisions relevant to the posting.
Sketch an AI platform design
As a practice exercise, diagram how technical and non-technical users could create, deploy, and manage agents and workflows. Explain interface choices, data access, and tradeoffs rather than presenting the design as Live Nation’s architecture.
Review data-platform integration choices
Prepare examples or design notes showing when RDBMS, Spark or Flink, Kafka, Lucene or Elasticsearch, Data Mesh, and Databricks fit. The posting names these technologies; be ready to distinguish direct experience from conceptual knowledge.
Build a production-readiness review
Draft a short checklist for reliability, observability, monitoring, security, and operations across AI workflows and persistent data. Include how you would detect failures and decide whether a deployment is ready.
Create a self-service enablement sample
Draft a brief non-technical user guide and a coaching plan for an AI-augmented workflow. Add proposed feedback and adoption, effectiveness, and ROI measures, which the role calls for.
Outline a cross-functional delivery example
Prepare a real example, if available, of prioritizing concurrent roadmap work, tracking it in Jira or a similar tool, and resolving decisions with Architecture, Data Engineering, or Security. Note how you coordinated across time zones.
Prepare questions about scope and decisions
Ask which AI tooling exists today, who its first self-service users are, how production standards and adoption are measured, and when the team builds tools versus evaluating vendors.

Availability last checked: Nov 4, 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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