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About This Role
Description:
Junior AI Operations Analyst — CNC Manufacturing
Location: Johnson Creek, WI
Job Type: Full\-time on\-site
Department: Operations / Engineering / Continuous Improvement
Reports To: Operations Manager, Engineering Manager, or IT/Systems Lead
Pay Range: $28\.00 \- $30\.00
About the Role
We are looking for an entry\-level AI Analyst to help our CNC manufacturing team use modern AI tools to improve daily operations, documentation, reporting, quoting, scheduling, quality tracking, and process improvement.
This is an entry\-level role for someone who is curious, organized, technically comfortable, and eager to learn how AI can support a real manufacturing environment. The ideal candidate should understand tools like ChatGPT, Claude, AI agents, prompt workflows, and the basics of connecting AI tools to business data through Model Context Protocols and APIs.
Key Responsibilities
- Use AI tools such as ChatGPT, Claude, and similar platforms to support business and manufacturing workflows.
- Help build and document AI\-assisted workflows for quoting, job setup, work instructions, inspection reports, purchasing, scheduling, and customer communication.
- Assist with creating prompts, templates, and reusable AI workflows for office and shop\-floor teams.
- Support basic AI agent use cases, such as retrieving information, summarizing data, preparing reports, and automating repetitive tasks
- Help connect AI tools to internal data sources, such as ERP, CRM, spreadsheets, SQL databases, or production tracking systems.
- Learn and assist with Model Context Protocol, also called MCP, for safely connecting AI tools to company systems and data.
- Work with operations, engineering, quality, and administration teams to identify practical AI opportunities.
- Review AI outputs for accuracy, usefulness, and alignment with company procedures.
- Help create internal documentation and training materials for AI tools.
- Maintain awareness of data privacy, security, and responsible AI use.
Manufacturing Use Cases This Person May Support
- AI\-assisted estimating and quote preparation.
- Summarizing customer RFQs and technical requirements.
- Creating draft work instructions from drawings, notes, or routing data.
- Organizing inspection and quality documentation.
- Searching internal procedures or job history using AI.
- Building AI assistants for sales, scheduling, purchasing, or production support.
- Connecting AI tools to databases, spreadsheets, ERP data, or document libraries.
- Automating routine reports for production, backlog, late jobs, scrap, rework, or purchasing needs.
Requirements:
Required Qualifications
- Beginner to intermediate familiarity with ChatGPT, Claude, or similar AI tools.
- Basic understanding of prompts, AI assistants, and AI agents.
- Familiarity with databases, spreadsheets, or business software systems.
- Interest in connecting AI tools to company data using MCP, APIs, SQL, or similar methods.
- Strong attention to detail and willingness to verify AI\-generated information.
- Ability to communicate clearly with both technical and non\-technical employees.
- Strong problem\-solving skills and curiosity about manufacturing processes.
Preferred Qualifications
- Exposure to CNC machining, manufacturing, job shops, fabrication, or industrial operations.
- Basic SQL knowledge or experience working with databases.
- Experience with Excel, Power BI, ERP systems
- Familiarity with automation tools such as Zapier, Make, n8n, or similar platforms.
- Basic scripting knowledge in Python, JavaScript, or another programming language.
- Understanding of APIs, webhooks, or system integrations.
- Experience creating SOPs, work instructions, reports, or process documentation.
Success in This Role Looks Like
- Employees are able to save time using simple, reliable AI workflows.
- AI tools are connected to useful company data in a safe and controlled way.
- Repetitive administrative and reporting tasks are reduced.
- Prompts, workflows, and AI agents are documented clearly.
- The company gains practical AI capability without disrupting shop operations.
Salary Context
This $58K-$62K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 2130 roles with salary data).
View full AI/ML Engineer salary data →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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Laureate Group, 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 $185,000 based on 13,200 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $97,760. This role's midpoint ($60K) sits 67% below the category median. Disclosed range: $58K to $62K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Laureate Group AI Hiring
Laureate Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Johnson Creek, WI, US. Compensation range: $62K - $62K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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