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About This Role
Do you want to join a company that:
- has been voted a Top Workplace in the Madison, WI area for the seventh year in a row?
- is a family-based company that values a work-life balance?
- has quarterly in-office chair massages available at no cost to its team members?
- has implemented solar panels to help reduce its carbon footprint?
At Amtelco, we believe in the importance of individuals, the diversity they bring, and the power of teamwork. We listen to our team members and select initiatives that are important to them. Join our family-owned business where we value work-life balance and create a fun, supportive, inclusive environment for our team members to succeed in!
Amtelco is the leader in developing SQL-based applications that are an open platform for critical interoperability in Healthcare and Commercial markets throughout the United States. We have received more than 30 U.S. Patents for telephony software, data handling methodologies, and telephone switching devices!
We’re actively seeking a skilled Senior Artificial Intelligence (AI) Developer in our Software department that will contribute to our mission of working with our family of customers to provide the very best products and support while creating a successful long-term partnership. As a Senior AI Developer, you’ll design, develop, and implement AI-powered applications and systems, focusing on integrating machine learning (ML) models and algorithms to solve real-world business problems. This role blends software engineering expertise with data science knowledge to create intelligent, autonomous solutions.
This full-time position is based at Amtelco’s corporate headquarters in McFarland, WI and reports to our VP of Software Development. Determined by your director, the benefit of a hybrid workplace model occurs at different times for each employee based on job performance.
How will I make an impact?
- Develop AI Models and Algorithms: Design, build, train, and fine-tune machine learning (ML) and deep learning models and algorithms for specific tasks like natural language processing (NLP), computer vision, and predictive analytics
- Data Handling and Preprocessing: Collect, clean, preprocess, and analyze large datasets to ensure high-quality, well-annotated data for model training
- Integrate AI into Applications: Deploy AI models into existing software systems and applications, often using APIs and cloud platforms to ensure seamless functionality and user experience
- Optimize and Troubleshoot: Monitor AI system performance, conduct testing, and debug issues to ensure reliability, scalability, and efficiency in production environments
- Collaborate and Communicate: Work closely with cross-functional teams, including data scientists, software engineers, and stakeholders, to define project requirements, align AI initiatives with business goals, and explain complex technical concepts in an approachable way
- Research and Innovate: Stay current with the latest advancements in AI technologies, frameworks, and industry trends to propose and implement innovative solutions
What does success look like in this role?
- Bachelor’s degree preferred, Associate degree required – educational focus in computer science, artificial intelligence, or a related technical field
- 5-8 years of relative experience
- Well-versed in Python, Java, C++, and/or R programming languages
- Strong understanding of machine learning (ML) algorithms, neural networks, and deep learning architectures
- Experience with AI frameworks and libraries, such as TensorFlow, PyTorch, and scikit-learn
- Familiarity with cloud computing platforms like Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP) for model deployment
- Profound understanding of data structures, algorithms, and software engineering best practices
- Strong analytical and problem-solving skills, critical thinking, creativity, and excellent communication and collaboration abilities
- Experience with healthcare and contact center environments and technology helpful
Amtelco complies with all federal laws regarding employment eligibility verification. All employees must maintain valid authorization to work in the United States throughout their employment. Employees whose work authorization is temporary are responsible for ensuring it remains valid and for providing updated documentation before the current authorization expires. Amtelco does not provide immigration sponsorship for work authorization (for example, H-1B, TN, or similar employment visas). Employment cannot continue if valid work authorization cannot be maintained.
Amtelco is proud to be an Equal Opportunity Employer. We prohibit discrimination or harassment of any kind and provide equal employment opportunities to all employees and applicants without regard to race, color, religion, sex (including gender identity or expression), sexual orientation, national origin, ancestry, age, disability, genetic information, military status, marital status, or any other characteristic protected by law. We also provide reasonable accommodations to qualified individuals with disabilities and those with sincerely held religious beliefs, in accordance with applicable laws. We are committed to a diverse and inclusive workplace where all individuals are valued for their unique backgrounds, skills, and perspectives.
Benefits:
- 401(k)
- Dental insurance
- Employee assistance program
- Flexible schedule
- Flexible spending account
- Health insurance
- Health savings account
- Life insurance
- Paid time off
- Vision insurance
Education:
- Associate (Required)
Experience:
- AI Development: 5 years (Required)
Ability to Commute:
- McFarland, WI 53558 (Required)
Work Location: Hybrid remote in McFarland, WI 53558
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Amtelco, 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Amtelco AI Hiring
Amtelco has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in McFarland, WI, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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