Interested in this AI/ML Engineer role at Bluemont Group, LLC.?
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About This Role
At Bluemont Group, our mission is simple: We care for every Guest, every visit, so they return often.
We believe our success starts with our people. Guided by our values of Respect, Integrity, Service, and Excellence (R.I.S.E.), we work each day to create positive experiences for our Guests, Team Members, and the communities we serve.
We are looking for an IT \& AI Systems Specialist to join our Home Office team in Knoxville. This person will be the go\-to technology resource for our Home Office employees and field leaders, providing day\-to\-day IT support across approximately 84 Dunkin’ restaurants.
Most of this position is focused on hands\-on IT support and technology operations. You will help solve hardware and software issues, set up and maintain equipment, manage technology purchases, and support new restaurant openings. You will also have the opportunity to help manage our company’s enterprise AI platform and create practical dashboards and tools that make work easier for our teams.
This could be a great fit for someone who enjoys solving problems, helping people, and finding better ways to use technology in the workplace.
What You’ll Do
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### IT Support and Operations
- Serve as the first point of contact for technology questions and issues from Home Office employees and field leaders.
- Troubleshoot hardware, software, account access, connectivity, printers, and other common technology issues.
- Set up, configure, deploy, and maintain laptops, desktops, printers, and related equipment.
- Manage user accounts, software installations, licenses, and device transitions for new hires and departing Team Members.
- Document issues, solutions, equipment assignments, and standard technology configurations.
- Create straightforward how\-to guides and resources that help Team Members solve common issues.
### Equipment Purchasing and Management
- Manage the purchasing process for laptops and other IT equipment.
- Identify equipment needs, gather vendor quotes, and complete purchases within approved budgets.
- Maintain an accurate inventory of company technology and track equipment throughout its lifecycle.
- Coordinate repairs, replacements, upgrades, and the retirement of outdated equipment.
### New Restaurant Openings
- Travel occasionally to new restaurant locations to assist with technology installation, setup, and testing.
- Work with vendors and Operations leaders to help ensure each restaurant is ready from a technology standpoint before opening.
- Help troubleshoot and resolve technology issues that arise during the opening process.
### AI Administration and Business Tools
- Administer Bluemont’s enterprise AI platform, including user access, account setup, and usage monitoring.
- Help ensure AI tools are used responsibly and in accordance with company policies.
- Provide practical guidance and light training to help teams use AI tools effectively.
- Build and maintain AI\-assisted dashboards and simple internal tools for teams such as Operations, Finance, and People.
- Look for practical opportunities to improve processes and make everyday work more efficient.
What We’re Looking For
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### Required Qualifications
- At least two years of hands\-on experience in IT support, desktop support, help desk support, or a similar IT role.
- Strong troubleshooting skills involving Windows computers, software, basic networking, and common business applications.
- Experience purchasing and managing technology equipment, or the ability to quickly take ownership of that responsibility.
- Familiarity with AI tools such as Claude, ChatGPT, or similar platforms.
- A strong customer\-service mindset and the patience to assist people with different levels of technical experience.
- Clear written and verbal communication skills.
- The ability to stay organized, manage multiple priorities, and follow issues through to resolution.
- A valid driver’s license and the ability to travel occasionally for new restaurant openings.
- The ability to work on\-site at Bluemont Group’s Home Office in Knoxville, Tn on Cedar Bluff.
### Preferred Qualifications
- Experience supporting a multi\-location restaurant, retail, or franchise organization.
- Experience administering Microsoft 365\.
- Experience managing an enterprise AI platform or developing AI\-assisted workflows.
- Experience creating dashboards or reports using Excel, business intelligence tools, or light scripting.
- Familiarity with restaurant technology or point\-of\-sale systems.
Additional Details
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- The position is based on\-site at Bluemont Group’s Home Office in Cedar Bluff, Knoxville.
- Travel is approximately 5% and is primarily connected to new restaurant openings within a three\- to five\-hour drive.
- The position may require lifting and moving computer equipment weighing up to 40 pounds.
Why Join Bluemont?
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You will have the opportunity to work with Team Members and leaders across the company and make a noticeable difference in how they use technology each day. This is a hands\-on role where you can solve immediate problems while also helping Bluemont find smarter and more efficient ways to work.
You will not be limited to one narrow area of IT. You will support people, manage equipment, participate in new restaurant openings, and help shape how our company uses emerging AI tools.
If you are approachable, dependable, curious about technology, and enjoy being the person others can count on when something is not working, we would love to hear from you.
Salary Context
This $55K-$68K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Bluemont Group, LLC., 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($61K) sits 72% below the category median. Disclosed range: $55K to $68K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Bluemont Group, LLC. AI Hiring
Bluemont Group, LLC. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Knoxville, TN, US. Compensation range: $68K - $68K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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