AI Automation Engineer

K Group CompaniesGrand Rapids, MI
Onsite

About The Position

The AI Automation Engineer is a core technical delivery resource within K Group’s AI and Automation practice. Working alongside the AI Enablement Team and practice leadership, this role leads the design, build, and ongoing optimization of AI-powered solutions for both internal operations and external client engagements. The engineer translates scoped strategy into production-grade systems, contributes to internal adoption efforts, and helps build the repeatable frameworks that allow the practice to scale. This is a builder role first. The ideal candidate thrives on turning complex business requirements into working, measurable systems — and takes pride in delivering solutions that are practical, responsible, and built to last.

Requirements

  • Hands-on expertise in AI/automation platforms — n8n, LLM integrations, workflow orchestration, API development.
  • Proficiency with Python and modern integration patterns across business platforms including ConnectWise, Microsoft 365, and SharePoint.
  • Experience building production-grade systems — not just proof-of-concept tools — with attention to reliability, documentation, and maintainability.
  • Strong communicator capable of presenting technical work and outcomes to non-technical stakeholders.
  • Comfortable working within a defined strategic framework while exercising autonomy in execution and delivery decisions.
  • Applicants must be legally authorized to work in the United States at the time of application. This position does not offer employment visa sponsorship now or in the future.

Nice To Haves

  • MSP or technology services background preferred.

Responsibilities

  • Design, build, and maintain AI-powered automation workflows using n8n, Python, and integrated APIs including ConnectWise, Microsoft 365, SharePoint, and others.
  • Develop intelligent systems including multi-stage AI classification pipelines, automated compliance and billing review processes, and notification-driven monitoring tools.
  • Implement provider-abstracted AI architectures supporting OpenAI, Ollama, and other LLM providers with configurable confidence thresholds and fallback logic.
  • Build and iterate on internal tools that reduce manual effort, improve data accuracy, and create measurable operational efficiencies.
  • Lead delivery execution for AI/automation client engagements — managing implementation, iteration, and ongoing optimization through to completion in coordination with the practice team.
  • Participate in client discovery sessions as a technical resource, contributing to needs assessment, feasibility evaluation, and solution design under the direction of practice leadership.
  • Translate scoped business requirements into working systems and present delivery results to client stakeholders including operations leads and project sponsors.
  • Build reusable delivery frameworks, templates, and documentation that allow AI consulting engagements to scale efficiently across multiple clients.
  • Support AI adoption across internal teams by building training materials, playbooks, and purpose-built tools that make AI accessible to non-technical staff.
  • Track and report on AI adoption metrics, connecting usage data to business outcomes rather than vanity metrics.
  • Contribute to responsible AI use and utilization efforts — including Microsoft 365 Copilot — supporting the governance frameworks and usage guidelines established by practice leadership.
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