Interested in this AI/ML Engineer role at K Group Companies?
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About This Role
About K Group Companies
K Group Companies is a locally owned and operated master integrator headquartered in Grand Rapids, Michigan, with a proud history dating back to 1980\. We support customers across the United States by delivering innovative, high\-quality technology solutions across managed IT, physical security, and integrated services.
As a third\-generation, family\-owned business, we’ve built our reputation on long\-term relationships, trusted expertise, and a commitment to doing things the right way for our customers and for each other.
We believe great work happens when people feel connected to their purpose, their team, and their growth.
Why K Group Companies?
At K Group Companies, culture is built on long\-standing relationships, both with our customers and with each other.
Our team is made up of people who take pride in solving problems, supporting one another, and delivering work that reflects the standards we’ve built our reputation on. Whether it’s designing secure environments for customers, supporting critical IT infrastructure, or collaborating across teams, we operate as one organization working toward shared success.
We also believe work should be fulfilling and enjoyable. From friendly gaming competitions in our Team Zone arcade to grabbing lunch together in the community, we value connection and teamwork just as much as technical excellence.
What you can expect here:
- A family\-owned company with deep roots in West Michigan
- A trusted advisor culture built over 40\+ years of relationships
- The opportunity to work across diverse, real\-world technology environments
- A team\-oriented culture grounded in accountability, collaboration, and pride in workmanship
- A workplace where people genuinely know and support each other
*We believe we are better together, and that belief shows up in everything we do.*
*Work Authorization Requirement:* *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.* *This role is 100% onsite in Grand Rapids, Michigan.* *No third\-party recruiters, agencies, or C2C arrangements.*
Role Overview
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.
Core Responsibilities
Automation \& Workflow Engineering
- 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.
Client Delivery \& Engagement Support
- 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.
Enablement \& Adoption
- 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.
What Success Looks Like — Year One
To be defined collaboratively with leadership. Initial indicators may include:
- Production\-grade automation workflows deployed for internal K Group operations.
- Client delivery engagements completed on time and within scope with documented outcomes.
- Reusable delivery frameworks and templates in place for at least two service offering types.
- AI adoption metrics established and actively tracked across internal teams.
- Contributed to AI utilization efforts, including Microsoft 365 Copilot, with adoption measurably improving across internal teams.
Experience \& Qualifications
- 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.
- MSP or technology services background preferred.
Reporting \& Structure
Reports to: Director of Engineering
Team: AI Enablement Team
Location: Grand Rapids, MI
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At K Group Companies, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
K Group Companies AI Hiring
K Group Companies has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Grand Rapids, MI, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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