Forward Deployed Engineer, Live Nation

• Minimum 5 years of experience in software engineering, solutions engineering, or technical product roles, with a record of delivering customer- or stakeholder-facing solutions
• Minimum 2 years of hands-on experience building, deploying and supporting LLM-based applications, including at least one agentic system running in production — ideally on Amazon Bedrock / Bedrock AgentCore or a comparable platform
• Strong proficiency in Python; working knowledge of Terraform and CI/CD (GitHub Actions preferred)
• Demonstrated product thinking: ability to scope, prioritize, and measure the impact of technical solutions
• Able to present technical trade-offs, cost and outcomes directly to executive and non-technical audiences, and to defend them under challenge
• Working data literacy — SQL and comfort operating in a lakehouse environment (Databricks preferred)
• Executive presence — excellent communication and interpersonal skills with the ability to engage senior business leaders and non-technical audiences
• Comfort operating independently in ambiguous, fast-moving environments

Desirable

• Live Nation Entertainment is seeking a Forward Deployed Engineer – Enterprise AI to serve as a product and technical lead embedded with business stakeholders across the enterprise
• This role reports directly to the Sr Director – Data & AI Engineering and is the bridge between business problems and production AI agents
• The Forward Deployed Engineer combines business acumen, product thinking, and agentic AI technical capability into a single role
• This leader will engage directly with senior business stakeholders to focus on high-impact AI use cases, then design, build, and ship production agents on the enterprise agentic AI stack — AWS Bedrock AgentCore, Amazon Bedrock, Databricks and associated control plane, governance & cybersecurity platforms
• This role carries responsibility for measurable value delivery: taking AI opportunities from discovery through production deployment and adoption, ensuring alignment with enterprise architecture, security, and governance standards
• The role owns the use case from discovery through first production release and adoption
• It builds on the enterprise agentic AI platform but is not accountable for platform uptime or the shared-service AI roadmap, which sit with the Agentic AI Engineering team
• Business Engagement & Product Leadership
• Partner directly with business unit leaders to discover, scope, and prioritize AI agent use cases with clear ROI
• Translate ambiguous business problems into well-defined agent products with success metrics
• Present solutions, demos, and outcomes to executive audiences with clarity and confidence
• Drive adoption: train users, gather feedback, and iterate rapidly toward measurable business value
• Agent Engineering & Delivery
• Design, build, and deploy production AI agents on Amazon Bedrock AgentCore (Runtime, Gateway, Memory, Identity, Observability), Amazon Bedrock and Databricks, together with the associated control plane, governance and cybersecurity platforms
• Develop tool integrations, MCP servers, RAG pipelines, and context engineering tailored to each use case
• Define success metrics and build the evaluation harness for each agent — offline eval sets, rubric and LLM-as-judge scoring, regression gates in CI, and online quality monitoring of production traffic
• Design for least-privilege agent identity, tool-scoping and guardrails from the outset, including defences against prompt injection and tool abuse, in line with Cybersecurity standards
• Build with enterprise standards: Python, Terraform for infrastructure, GitHub and GitHub Actions for CI/CD
• Ensure every deployed agent is secure, observable, cost-efficient, and supportable, in partnership with the Agentic AI Engineering team
• Enterprise Collaboration
• Work alongside Data Engineering to leverage the Databricks lakehouse and enterprise data assets
• Coordinate with Cybersecurity, Architecture, and Operations to meet governance, privacy, and compliance requirements
• Contribute learnings back to reusable templates, patterns, and platform capabilities
• MEASURES OF SUCCESS - FIRST 12 MONTHS
• 3 agent use cases taken from discovery to production with documented business value
• Defined success metrics and an operating evaluation harness for every agent shipped
• Documented adoption against target user population for each deployed agent
• Reusable patterns and templates contributed back to the Agentic AI Engineering platform