Interested in this AI/ML Engineer role at K Group Companies?
Apply Now →Skills & Technologies
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 Junior AI \& Automation Engineer is a hands\-on builder role within K Group’s AI and Automation practice. This role focuses on the design, construction, and iteration of automation workflows, AI\-assisted integrations, and internal tooling that the practice delivers to clients and deploys internally.
This is not a help\-desk role. It’s not a ticket queue. It’s a make\-things\-work role for someone who gets excited about connecting systems that weren’t designed to talk to each other — and figures out what’s possible when they do. The ideal candidate thrives on turning ambiguous problems into working, practical systems and takes ownership of what they build.
Core Responsibilities
Automation \& Workflow Engineering
- Design, build, and maintain automation workflows using n8n, APIs, and webhook\-based integrations across business platforms.
- Develop AI\-assisted pipelines including document processing, classification, notification routing, and data transformation workflows.
- Connect disparate systems — CRMs, ticketing platforms, ERPs, communication tools — that weren’t designed to work together.
- Build and iterate on internal tools that reduce manual effort and create measurable operational efficiencies for K Group and its clients.
Client Delivery Support
- Assist with delivery execution on AI/automation client engagements under the direction of practice leadership.
- Participate in client discovery sessions as a technical contributor, supporting needs assessment and solution scoping.
- Translate defined business requirements into working systems and document outcomes clearly for client and internal stakeholders.
- Contribute to reusable delivery frameworks, templates, and runbooks that allow the practice to scale across multiple client engagements.
Internal Enablement
- Build lightweight dashboards and front\-end tools that surface automation outputs to non\-technical users.
- Document what you build — workflows, integration maps, configuration notes — so solutions are maintainable and transferable.
- Support internal AI adoption by contributing to training materials and usage playbooks as the practice grows.
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.
- At least two client delivery engagements contributed to or completed on time and within scope.
- Reusable workflow templates or delivery components documented and handed off to the practice library.
- Clear growth path identified toward a senior engineering role within the AI \& Automation practice.
Experience \& Qualifications
- Has built something for the joy of building it — a homelab, a personal automation, a self\-hosted stack, a side project. Show us something.
- Comfortable working with APIs, webhooks, and JSON without needing a tutorial for every call.
- Familiar with Linux environments and capable of reading documentation when there’s no walkthrough available.
- Communicates clearly — can explain what something does and why it was built that way.
- Doesn’t need a strict playbook. Figures things out.
Bonus qualifications:
- Hands\-on experience with n8n, Make (Integromat), Zapier, or similar automation platforms.
- Familiarity with AI/LLM tooling — OpenAI API, Ollama, Anthropic, LangChain, or similar.
- Python or JavaScript scripting at a working level (not a developer, but comfortable in code).
- Background in IT, MSP, sysadmin, or network environments.
- Docker or self\-hosted deployment experience.
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. Entry-level AI roles across all categories have a median of $97,880.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.