Software Engineer, AI-Native Builder

Role Overview

This is not just a hands-on coder role — it is a force multiplier. You will ship product features while

also being the person who has the opportunity to fundamentally change how the entire

engineering team works. You will own, extend, and continuously evolve the company’s proprietary

AI toolkit while leading a company-wide SDLC rebuild powered by AI agents. You will mentor a

distributed offshore team of senior software engineers on using AI tools, design the AI

infrastructure environment for the entire engineering organization, and build AI-powered features

directly into the home services SaaS product. The right person for this role has actually changed

how a team works before — not just used AI tools themselves. The stated goal: 10x engineering

productivity.

About the role

You will pair closely with our engineers, who are already AI-native, and together you become

the AI center of gravity on the team. The offshore team is made up of strong, experienced

software engineers who need mentoring to using AI to its full potential — you are the person

who changes that. You also own the toolkit infrastructure that makes AI work reliably across

the whole organization: the agents, the skills, the context pipelines, and the MCP integrations.

Your impact is measured not just by what you ship, but by how much faster everyone else ships

because of you — and by whether the AI toolkit itself is getting smarter over time.

What you'll do

Moon AI Toolkit — Ownership & Evolution

 Own, maintain, and continuously evolve the company’s Moon AI Toolkit

 Build new agents from scratch — define agent scope, system prompts, tool access, and

evaluation criteria

 Write new skills (e.g. Claude Code native skills) that are automatically discovered and invoked

across the engineering workflow

 Design new multi-agent workflows that orchestrate specialists in parallel and sequentially to

complete complex engineering tasks

 Maintain and improve the agent routing system — ensuring the right agent is dispatched for

every task type, with clear escalation paths

 Evaluate agent performance continuously — identify failure modes, rewrite underperforming

agents, and log learnings to the shared knowledge base

SDLC Rebuild with AI Agents

 Lead the redesign of the company’s SDLC using AI skills and agents as the primary mechanism

of change

 Automate or AI-augment every repeatable SDLC step: ticket refinement, code review, test

generation, documentation, and deployment verification

 Work directly with the engineering team to roll out changes company-wide — including

training, change management, and feedback loops

 Define the measurable productivity baseline and track progress against the stated 10x

improvement goal

 Own the rollout roadmap: from POC phase (first 90 days) through team-wide adoption

Full-Stack Feature Delivery

 Work across the .NET / C# backend (ASP.NET, EF Core), Python, TypeScript / Capacitor frontend

(cross-platform mobile), and AI integration layer (LLM APIs, RAG, agent pipelines)

 Build AI-powered features into the product directly — home services use cases including

scheduling intelligence, recommendations, and workflow automation

 Maintain production quality throughout: tests, documentation, and code review for every

feature shipped

AI Environment for the Engineering Organization

 Design and implement the infrastructure and tooling environment that makes successful AI

usage possible across all engineers

 Own MCP (Model Context Protocol) server configuration and management — the integration

layer connecting AI agents to internal systems (Jira, Confluence, GitHub, Slack, Notion)

 Standardize IDE plugin configuration and AI assistant settings across the team

 Design and maintain context injection pipelines — ensuring AI agents have access to accurate,

up-to-date project context at all times

 Own the onboarding program for new engineers joining the AI-assisted workflow

Context Building & Knowledge Management

 Implement engineering org-wide context layer best practices: structured context files

(.claude/docs/ — project-map, known-issues, conventions, decisions, lessons), shared

knowledge management, and AI tool configuration standards

 Own the prompt library governance process — curate, version-control, and share high-value

prompts across the team

 Establish standards for how agents consume context: what goes in knowledge files, how to

structure agent instructions, and how to keep context current as the codebase evolves

Team Enablement & AI Adoption

 Mentor and upskill engineers on AI tooling

 Define and roll out AI-assisted development standards across the whole engineering team (e.g.

Cursor, Copilot, or equivalent)

 Establish code quality standards and review practices that scale with AI-assisted development

 Help translate poorly defined or ambiguous tickets into clear, executable engineering tasks

before work begins

Qualifications

Demonstrated experience driving AI tooling adoption across an engineering team — with

measurable outcomes, not just personal usage

 Deep proficiency with AI-assisted coding tools like Cursor, GitHub Copilot, Claude Code, etc. —

you use these daily, not occasionally

 Experience building and evolving AI agent systems: agent definitions, multi-agent orchestration,

routing logic, and failure mode analysis

 Enough .NET / C# fluency to be credible and effective with a senior engineering team — you

can review their code and spot issues

 TypeScript / Capacitor for frontend and cross-platform mobile work — you own the full stack

for AI-powered features

 Experience integrating LLM APIs into production applications (OpenAI, Anthropic / Claude,

Azure OpenAI, or similar)

 Understanding of Model Context Protocol (MCP) — confi