AI Enablement Engineer

At AppWork, we’ve built a simple, user-friendly PropTech platform that modernizes maintenance operations. Our platform brings everything into one easy-to-use platform so property managers, maintenance teams, and residents can all enjoy a smoother, more connected experience.

As a member of the AppWork team, you’ll be helping to build and deliver technology that solves real problems, makes communities run better, and creates happier teams and residents.

The Opportunity

We're looking for an AI Enablement Engineer to make our R&D organization great at building with AI.

This is a hands-on engineering role with a multiplier mandate, not a research role or a model-building role. You'll write real code and ship real things, but your bigger job is to raise the AI fluency of around 20 engineers across our squads.

You'll own how we use agentic coding tools, build the internal tooling and context that makes those tools actually useful in our codebase, and set the standards that turn "I tried the AI thing once" into a measurable lift in how fast and how well the team ships.

If you live inside these tools every day, get genuinely excited about the next capability drop, and also have the patience to bring other engineers along with you, this role is built for you.

What You'll Do (Responsibilities)

• Drive adoption of AI coding tools across the team: own our usage of Claude Code, Cursor, and similar agentic tools, define what excellence looks like and help each squad fold these tools into their day to day work.
• Build and maintain our AI context layer: create and curate the shared knowledge that makes agents effective in our repos: per repo context files, reusable prompts, skills, and house rules that encode how we actually build.
• Develop internal tooling and agents: build MCP integrations, custom agents, scripts, and workflows that automate the routine parts of engineering and connect our tools into something that saves real hours.
• Teach and level up the team: run enablement sessions, write clear playbooks, pair with engineers, and tailor guidance by experience level so a senior and a junior each get what they need.
• Measure the impact: track what's working with real signals (cycle time, review load, throughput, quality), so AI adoption is grounded in outcomes..
• Set guardrails: define safe, compliant patterns for AI tool use that respect our security and data handling standards. Make the responsible path the easy path.
• Stay ahead of the curve: the tooling landscape changes weekly; evaluate new tools and models, run quick pilots, and bring the team proven wins instead of hype.
• Contribute to the development of AppWork's foundational framework for AI features, including the evaluation platform, logging, and visibility systems.
  
  

• Hands-on, daily use of agentic AI coding tools (Claude Code, Cursor, Codex, Copilot, etc.) as your primary development workflow. More importantly, you have successfully led AI adoption across an R&D team by training engineers, creating standards and playbooks, measuring adoption, and improving engineering velocity - not just using the tools yourself.
• Experience driving AI adoption across an engineering organization of 10+ software engineers, including influencing senior engineers and technical leaders.
• Practical LLM building experience: you've built with LLM APIs and understand prompt engineering, context engineering, tool/function calling, and retrieval. You know how to get reliable results out of non deterministic systems.
• You've built and shipped custom skills, subagents, commands, and MCP servers for real production grade workflows, not tutorial projects, and you can talk concretely about where these tools fail and how you worked around it.
• At least 7 years of experience in full stack development, with a strong track record of delivering production-grade SaaS applications.
• Strong software engineering fundamentals: you can read an unfamiliar codebase, ship production quality code, and earn the technical respect of senior engineers. This is not a non-coding role.
• A teaching instinct: you explain technical ideas clearly and enjoy helping other people get better. You can write a playbook that the team will actually follow.
• A bias for measurable impact: you care whether something actually moved the needle, and you can show it.
• A security and quality mindset: you think about what could go wrong with data, access, and generated code before it becomes a problem.
• Fluent English for written documentation and team communication.
• Bachelor's degree in Computer Science, Software Engineering, or a related technical field.
  
  

• By 90 days: every squad has a working AI setup and clear guidelines, our core repos have solid context files in place, and engineers know who to go to and where to look when they want to use these tools well.
• By 6 months: using AI tools well is simply how the team builds, not a special effort. We can point to concrete improvements in delivery speed and quality, and we have a steady pipeline of internal tooling that keeps removing friction.