Quick overview
Job description
AI Integration Engineer/FDE
We are looking for a motivated AI Integration Engineer with IT or software engineering experience to work directly with client engineering teams in an enterprise environment. This is a hands-on, client-facing role focused on deploying AI-powered developer tools, building custom integrations, developing automation, and troubleshooting technical solutions. The ideal candidate is technically curious, comfortable working in fast-paced environments, and passionate about emerging AI technologies and developer tooling.
Responsibilities
- Work onsite with client engineering teams to understand workflows, technical challenges, and integration requirements.
- Configure, deploy, customize, and troubleshoot AI coding tools such as Devin, Windsurf, Claude Code, and GitHub Copilot.
- Build and configure Model Context Protocol (MCP) servers and clients to connect AI agents with internal tools, APIs, and databases.
- Set up secure connectivity using ngrok, Cloudflare Tunnel, SSH tunneling, and similar technologies.
- Develop Python scripts, automation, and API integrations to connect AI agents with enterprise systems.
- Assist with LLM adaptation and fine-tuning activities, including LoRA, PEFT, prompt tuning, dataset preparation, and model evaluation.
- Rapidly develop prototypes and proof-of-concepts based on client requirements and feedback.
- Troubleshoot and debug integrations in live client environments.
- Create technical documentation and provide technical enablement and training to client teams.
- Gather client feedback and communicate technical requirements and challenges to product and engineering teams.
Requirements
<>Required Skills- 1–2 years of professional IT or software engineering experience.
- Must be local to Juno Beach, FL, or willing to relocate at their own expense before the start date.
- Must be available to work onsite from Day 1. No remote or hybrid option.
- Working knowledge of AI coding assistants such as Devin, Windsurf, Claude Code, GitHub Copilot, or similar tools.
- Understanding of MCP (Model Context Protocol) architecture, including experience building or consuming MCP servers.
- Hands-on experience with ngrok, Cloudflare Tunnel, SSH port forwarding, or similar tunneling technologies.
- Strong Python skills for scripting, automation, and API integration.
- Exposure to LLM fine-tuning, including LoRA/PEFT, dataset preparation, and model evaluation.
- Familiarity with Git/GitHub and standard version-control practices.
- Basic understanding of REST APIs, webhooks, and cloud platforms such as AWS, Azure, or Google Cloud Platform.
- Experience with Docker, containerization, and CI/CD pipelines.
- Exposure to vector databases and RAG (Retrieval-Augmented Generation).
- Prior client-facing, consulting, or technical support experience.
- Familiarity with prompt engineering and agentic AI workflows.
- Experience rapidly developing technical prototypes and proof-of-concepts.
- Strong communication and interpersonal skills.
- Comfortable working directly with clients and technical stakeholders.
- Ability to work independently in ambiguous and fast-paced environments.
- Strong troubleshooting and problem-solving abilities.
- Comfortable debugging live technical issues under pressure.
- Strong curiosity and willingness to learn emerging AI technologies.
- Creative and solution-oriented mindset.
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