L
Live Nation
Lead Engineer (Agentic AI Engineering)
Skills
Data EngineeringE-commerce WebsitesPythonDatabricksCybersecurityAIAWSGitHubWorkdayComplianceTerraform
1 month ago
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Important information
Contract type:
Permanent
Salary:
$176,000 – $220,000 / year
Location:
Beverly Hills, CA, USA
Work mode:
Onsite
Published on:
12 September 2026
What they need
- Live Nation Entertainment is seeking a hands-on, execution-focused technical leader to serve as Lead Engineer – Agentic AI Engineering. This role reports directly to the Sr Director – Data & AI Engineering and owns the engineering, reliability, and lifecycle management of the enterprise agentic AI platform
- The Lead Engineer is accountable for the design, build, operation, and financial stewardship of production AI agents on the AWS Bedrock AgentCore and the associated control plane, governance & cybersecurity platforms. This leader will establish engineering standards, own the agent lifecycle from development through retirement, and build out a small team of engineers to scale the practice over time
- This role carries full responsibility for the technical health, cost efficiency, and supportability of agentic AI workloads, ensuring alignment with enterprise architecture, security, and governance standards
- This role owns the platform — engineering standards, reusable templates, runtime reliability, FinOps, and the promotion gate through which agents built by Forward Deployed Engineers reach production. It does not own individual business use cases or stakeholder relationships
- Provide end-to-end technical ownership of the enterprise agentic AI stack:
- Amazon Bedrock AgentCore (Runtime, Gateway, Memory, Identity, Observability, Evaluations and Policy), Amazon Bedrock, and the associated control plane, governance & cybersecurity platforms
- Design, develop, and maintain:
- Production AI agents and multi-agent orchestration patterns
- Agent lifecycle management — versioning, evaluation, promotion, monitoring, and retirement, including offline eval sets, regression gates in CI and online quality monitoring of production traffic
- Model Context Protocol (MCP) servers, tool integrations, and secure API gateways
- Retrieval-Augmented Generation (RAG) pipelines and context engineering frameworks
- Establish reusable agent templates, reference architectures, and enterprise AI services
- Embed security, compliance, and privacy controls into agent pipelines in alignment with enterprise governance standards
- Own agent-level security controls: least-privilege agent identity, tool scoping and policy enforcement, guardrails, and defences against prompt injection and tool abuse — in partnership with Cybersecurity
- Ensure production-grade reliability, observability, and monitoring of agentic workloads
- Own FinOps for the AI platform: cost visibility, token and compute optimization, capacity planning, and chargeback/showback reporting
- Define and operate support models (runbooks, escalation paths, SLAs) for agents in production
- Participate in the on-call rotation for agentic workloads and in incident response alongside Cybersecurity and Global Technology Operations
- Champion “everything as code”: Terraform for infrastructure, GitHub and GitHub Actions for CI/CD, Python as the primary language
- Hire, onboard, and mentor a small team of engineers as headcount is approved, building the foundation of the Agentic AI Engineering practice
- Partner with Data Engineering, Cybersecurity, Architecture, and Operations teams on platform integration, including the Databricks lakehouse environment
- MEASURE OF SUCCESS - FIRST 12 MONTHS
- Published engineering standards, reference architecture and reusable agent templates in production use
- A working promotion gate — documented eval, security and cost criteria every agent must clear before release
- Unit cost per agent invocation baselined, with chargeback/showback reporting operating monthly
- Defined SLOs, runbooks and escalation paths for all production agents
- 2 engineers hired and onboarded
Benefits
- HEALTH: Medical, Dental and Vision benefits for you and your family, including Flexible Spending Accounts (FSA) and Health Savings Accounts (HSAs)
- YOURSELF: Paid time off policy including holidays, sick time, and day off for your birthday, free concert tickets
- WEALTH: 401(k) program with company match, Stock Program Reimbursement
- CAREER: Tuition reimbursement, student loan repayment internal growth and development programs & trainings
- FAMILY: New parent programs & support including caregiver leave and baby bonus, infertility support
- OTHERS: Volunteer time off, crowdfunding network- Passionate and motivated. Driven, with an entrepreneurial spirit. Resourceful, innovative, forward thinking and committed
- Practical experience securing LLM and agent systems: identity and least-privilege tool access, guardrails, and prompt-injection and tool-abuse mitigation
- Consumer e-commerce domain exposure preferred
- Experience hiring, mentoring, and leading small engineering teams
- Contributions to open-source agent frameworks or the MCP ecosystem preferred
- Experience across a multi-model foundation-model estate, including model selection on cost/quality trade-offs preferred
- Demonstrated experience with cloud cost management / FinOps for AI or data workloads
- Deep proficiency in Python, Terraform, and modern CI/CD practices (GitHub Actions preferred)
- Minimum 2 years of hands-on experience building LLM-based applications including at least one agentic system operating in production — ideally on Amazon Bedrock / Bedrock AgentCore or a comparable platform
- Excellent communication skills with the ability to explain complex agentic architectures to technical and non-technical audiences
- Minimum 10 years of experience in software or platform engineering, with a track record of shipping and operating production systems at scale
- Eval-driven development — offline eval sets, rubric and LLM-as-judge scoring, regression gates in CI, and online quality monitoring of production agents
- Live entertainment, ticketing, or large-scale
- Experience operating on the Databricks lakehouse platform preferred
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