AI Enablement Engineer

topgolf; United States; On-site; USD 120,000 - 160,000; Full time

Benefits offered by this job

Free Play

Half-price food

Health insurance

Dental insurance

Vision insurance

401(k) match

Mental well-being platform

Job summary

Topgolf is seeking an AI Enablement Engineer to help teams across the business unlock value from AI while upholding engineering standards. You will backstop AI outputs, review code and specs, and ensure secure, compliant delivery through Topgolf's AI-SDLC pipeline.

You will build archetypes, templates, and integrations, connect agents to data sources, and monitor for drift and risk. This is a cross-team enablement role with strong technical and communication expectations.

Qualifications

• Bachelor's degree or equivalent practical experience in CS/engineering.
• 3+ years of professional software engineering experience.
• Strong grounding in automated testing, version control, code review, and secure coding.
• Hands-on experience with LLM agent tooling and related frameworks.
• Experience with retrieval-augmented generation and vector databases.
• Ability to explain technical tradeoffs to non-technical stakeholders.
• Experience designing templates or tooling for reuse by others.

Responsibilities

• Collaborate with teams to unblock AI adoption and configure agent access.
• Design archetypes, skills, and templates for reusable AI-SDLC components.
• Connect agents to internal systems via governed connectors.
• Monitor models for drift and prompt issues, keeping code current.
• Review AI-generated code, specs, and gate results for correctness and safety.
• Test against adversarial prompting and implement guardrails.
• Build documentation, guides, and light training for teams.
• Triage escalations with security and data owners according to policy.

Skills

Python

Go

TypeScript

Java

APIs

Code review

Secure coding

LLM tooling

RAG

Vector databases

Education

Bachelor's degree in Computer Science, Engineering, or related field

Tools

Claude Code

GitHub Copilot

LangGraph

MCP

Job description

The AI Enablement Engineer helps teams across Topgolf, technical and non-technical alike, get real value from AI by removing the friction that blocks adoption and building the reusable archetypes, skills, and governed integrations that let business teams build safe, working applications through Topgolf's AI-SDLC pipeline. This role acts as the technical backstop, reviewing AI-generated code and specs for correctness, security, and adherence to engineering standards before anything ships.

• Partner with teams across Topgolf, technical and non-technical, to unblock AI adoption by configuring agent access, resolving friction, and showing teams how to get real value from available AI tools
• Design and build archetypes and skills (e.g., SKILL.md files, intake questionnaires, spec templates, code scaffolds) that package engineering standards and governance into reusable building blocks for the AI-SDLC pipeline
• Connect agents to internal systems, data sources, and tools through governed connectors and MCP-style integrations
• Monitor archetypes and agents for model drift, prompt bloat, and other degradation as underlying models and usage evolve, and keep code scaffolds and prompts current with secure coding practices and platform engineering standards
• Review AI agent-produced code, specs, and gate results for correctness, security, scope adherence, and good development practice before production
• Test archetypes and agents against adversarial prompting (prompt injection, jailbreak attempts) and implement guardrail hooks to enforce scope, safety, and policy compliance
• Build documentation, examples, and light training (office hours, walkthroughs, guides) to raise teams' baseline capability with AI
• Retire, merge, or extend archetypes based on usage and escalation patterns, and triage escalations to security, data owners, or app stewards, partnering with platform engineering so new archetypes and integrations fit the pipeline's runtime and release mechanics
• Triage escalations and route findings to security, data owners, or app stewards, following the pipeline's escalation rules
• Partner with the platform engineering team so new archetypes and integrations fit the pipeline's constrained runtime, connector catalog, and release mechanics

Required Skills and Experience

• Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience
• 3+ years of professional software engineering experience, with strong proficiency in Python and working knowledge of at least one other widely used language such as Go, TypeScript, or Java
• Strong grounding in core software development principles: automated testing, version control, code review, secure coding practices, and API design
• Hands on experience with LLM agent tooling, for example Claude Code, MCP, LangGraph, or a comparable framework, including prompting, tool use, and agent orchestration
• Experience with retrieval-augmented generation (RAG) and vector databases, and judgment for when they are the right tool versus a simpler approach
• Ability to evaluate AI generated code for correctness and risk, and to explain technical tradeoffs to non-technical stakeholders in plain language
• Experience designing templates, documentation, or tooling that other engineers or non-experts build from
• Comfort operating as both a broad enabler, unblocking adoption across many teams, and a careful reviewer, gating what individual teams ship

Preferred Qualifications

• Experience setting up and administering AI coding tools or agent platforms, such as Claude Code, GitHub Copilot, or Cursor, across a team or company
• Familiarity with the Claude Agent SDK, Model Context Protocol (MCP), or comparable agent frameworks such as LangGraph, CrewAI, or AutoGen
• Experience building internal AI adoption programs: office hours, documentation, champion networks, or similar
• Background in code review, static analysis, or security review, enough to interpret gate findings without necessarily building the scanners
• Experience contributing to governance, approval, or escalation workflows for a technical platform

What Success Looks Like

• Teams across Topgolf, not just engineering, are visibly getting more done with AI than they were a quarter ago, and can point to you as part of why
• Business teams build safe, working applications inside the pipeline instead of routing every idea to engineering
• The archetypes you maintain stay current, well scoped, and easy for non-technical builders to use without guessing
• What ships has already been checked against good software development practice, because you caught what the automated gates could not
• The archetype catalog and the tools you connect evolve based on evidence: what teams actually need, and what keeps showing up in questions and escalations

BENEFITS

Free Play & 1/2 price food! Health, dental, vision, 401(k) team member match, free mental well-being platform - and that's just for starters for those who qualify. View team member b