Summary
✨ AI‑Generated
A fast-growing, founder-led organization is seeking a highly capable Full-Stack AI Implementation Engineer to become its technical backbone. The role spans AI agents and LLM systems, CRM architecture, web development, custom automation, and business intelligence. You will translate high-level business challenges into reliable, fully deployed solutions supporting internal operations and external clients.
Highlights
Broad, high-impact engineering role with substantial ownership across AI, automation, web development, CRM architecture, and business intelligence. Offers the opportunity to work directly on practical business problems and deploy end-to-end technical solutions.
Description
About the Business & The Role
We are a fast-growing, founder-led business specializing in M&A, allied health services, and practical AI implementation.
The founder operates an ecosystem of businesses—including a speaking and coaching arm—where technology is our core competitive advantage.
We are seeking a highly capable Full-Stack AI Implementation Engineer to serve as the technical backbone across these ventures.
This is not a narrowly defined software role; it is a broad, hands-on, founder-facing position covering AI agents, GoHighLevel CRM architecture, web development, custom automation, and business intelligence.
You will act as the go-to technical partner, translating high-level business problems into fully deployed, reliable technical solutions for both internal operations and external clients.
Core Responsibilities
AI Agent & LLM Development: Design, build, deploy, and maintain AI-powered systems for lead qualification, internal ops, and customer service.
Take AI from concept to deployment in real business environments—going far beyond simple ChatGPT prompting.CRM & Automation Architecture: Configure and maintain GoHighLevel (GHL) environments.
Build sophisticated pipelines, workflows, and integrations to eliminate manual administrative work.Full-Stack Web Management: Develop, maintain, and iterate on portfolio and client websites, ensuring seamless integration with CRMs, analytics, and AI systems.Business Intelligence & Dashboards: Build decision-support systems (Looker Studio, Retool, Power BI) that translate raw data into actionable insights for the founder and our clients.Client-Facing Implementation: Translate non-technical client requirements into technical AI roadmaps.
Deploy, test, and troubleshoot AI workflows for external clients, explaining complex concepts in practical terms.Strategic Technical Partnership: Act as the technical right-hand to the founder.
Research emerging tools, build prototypes, and independently determine the best architecture to solve given business problems.
Required Experience & Qualifications
Proven Implementation: Demonstrated experience taking AI/LLM solutions (AI agents, RAG systems, OpenAI/Claude APIs) from concept to live deployment in business environments.CRM Mastery: Hands-on experience with GoHighLevel (or comparable platforms like HubSpot/Salesforce) and workflow automation (Zapier, Make, n8n).Web & Integration Skills: Strong understanding of web development (JavaScript/TypeScript, Python, HTML/CSS), API integrations, webhooks, and modern CMS platforms.Data Visualization: Experience building custom dashboards and reporting tools using SQL and BI platforms.Autonomy & Problem Solving: Exceptional ability to work independently in a remote environment, diagnosing complex integration issues without needing a perfectly written technical specification.Communication: Flawless written and verbal English, with the ability to communicate technical realities to non-technical stakeholders clearly.
Who We Are Looking For
We are looking for a builder, not just a technology enthusiast.
You thrive in ambiguity and hold yourself to a perfectionist standard—meaning your goal isn't just "it works," but rather "it works reliably, it's scalable, and it's documented."
You should be the kind of engineer who can hear, "We have this business problem, can we automate it?" and independently figure out the root problem, the ideal tech stack, the deployment strategy, and the success metrics.
