Bachelor's degree in Computer Science, Engineering, or related field, or equivalent experience.
3+ years of professional software engineering experience, proficient in Python and another language (Go, TypeScript, or Java).
Strong understanding of software development principles: automated testing, version control, code review, secure coding practices, and API design.
Hands-on experience with LLM agent tooling (e.g., Claude Code, MCP, LangGraph) including prompting and agent orchestration.
Experience with retrieval-augmented generation (RAG) and vector databases, with the ability to assess their appropriate use.
Ability to evaluate AI-generated code for correctness and communicate technical tradeoffs to non-technical stakeholders.
Experience designing templates and documentation for engineers and non-experts.
Responsibilities
Partner with teams to facilitate AI adoption by resolving friction and demonstrating value from AI tools.
Design and build reusable archetypes and skills for the AI-SDLC pipeline, incorporating engineering standards and governance.
Connect agents to internal systems and tools through governed integrations.
Monitor archetypes and agents for model drift and ensure compliance with secure coding practices.
Review AI-generated code and specifications for correctness and adherence to development standards before production.
Test archetypes against adversarial prompting and implement safety measures to enforce compliance.
Create documentation and training materials to enhance teams' capabilities with AI.
Benefits
Free Play & 1/2 price food.
Health, dental, and vision insurance.
401(k) team member match.
Access to a free mental well-being platform.
Full 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 benefits here.
