Interested in this AI/ML Engineer role at Andromeda?
Apply Now →About This Role
Some people use AI.
Some people can't stop building with it.
If that's you, keep reading.
Maybe you've built an AI agent just because you wanted to see if you could.
Maybe you've automated part of your life because doing it manually annoyed you.
Maybe you've connected systems that were never designed to work together.
Maybe you've stayed up until 2:00 AM because you had one more idea you wanted to try.
Good.
Keep reading.
What We're Building
For more than 30 years, Andromeda has helped manufacturers use technology to improve their businesses.
Now we're building something new.
We're launching an AI practice focused on helping manufacturers understand where AI creates real business value \- and where it doesn't.
We're not chasing AI because it's exciting.
We're helping companies solve real business problems.
You'll be one of the first people on that journey.
Not joining an established AI department.
Helping build one.
Working directly with our CEO, you'll explore new technologies, build internal tools, prototype solutions, and help shape the services we'll eventually deliver.
Some days you'll build.
Some days you'll research.
Some days you'll prove an idea doesn't work.
That's success too.
This Isn't a Traditional Engineering Job
If you're looking for clearly defined requirements, predictable work, and one technology to master...
This probably isn't your job.
If you're excited by ambiguity, enjoy figuring things out, and love building things that didn't exist yesterday...
You may have just found your next opportunity.
AI is evolving almost daily.
That means we'll be learning together.
There isn't a playbook.
We're building it.
What You'll Actually Do
Your work will evolve as AI evolves, but you'll spend most of your time:
- Building internal AI tools that make Andromeda more efficient.
- Experimenting with emerging AI technologies and determining where they create real business value.
- Building prototypes and automation using AI, APIs, MCP servers, and modern integration platforms.
- Helping evaluate and build AI solutions for clients as our practice grows.
- Documenting what works so we can build repeatable, scalable services.
Some weeks you'll build three things.
Some weeks you'll spend most of your time learning something completely new.
Both are valuable.
The Kind of Person We're Looking For
We care far more about curiosity than years of experience.
We're looking for someone who:
- Builds things because they enjoy figuring them out.
- Learns new technology quickly and isn't intimidated by change.
- Solves business problems, not just technical ones.
- Can explain technical ideas to non\-technical people.
- Naturally asks, "I wonder if..."
You don't need to know everything.
Nobody does.
You just need to love learning faster than the technology changes.
Here's What Would Excite Us
We're much more interested in what you've built than the path you took to build it.
Things we'd love to see include:
- AI projects, automations, integrations, or software you've built.
- Experience writing software in at least one modern programming language.
- Experience working with APIs or connecting different systems.
- A habit of experimenting with modern AI tools and technologies.
- A GitHub profile, portfolio, or examples of projects you're proud of.
A computer science degree is great.
Being self\-taught is great.
We care far more about your ability to learn, build, and solve problems than where you learned those skills.
Why This Role Is Different
You'll help shape a brand\-new practice inside an established technology company.
You'll work directly with our CEO.
Your ideas will matter.
You'll influence what we build, how we build it, and how our AI practice evolves.
Honestly...
We don't know exactly what this role looks like three years from now.
We hope that's because of the contributions you've made.
Salary Expectations
$75,000–$95,000, based on your experience, demonstrated technical ability, and the AI solutions you've built.
Not Sure?
If you're reading this thinking...
*"I'm not sure I'm qualified..."*
...but you're excited by what you've read...
Apply anyway.
The right mindset is more difficult to teach than the right technology.
We're much more interested in finding the right person than checking every box.
About Us
- Company: A 32\-year\-old technology service firm with a dedicated team of nearly 40 members.
- Mission: Providing outsourced IT services to ensure client satisfaction.
- We work in the HQ office 2x per week and work from home 3x per week (First 60\-90 days are generally 5x in the office)
Compensation, Benefits, and Perks
- Salary: $75K\-$95K.
- Benefits: Health, dental, vision insurance, 401K matching, life and disability coverage, paid holidays, sick days, vacation, mentorship program, and more. Flex vacation after 1 year.
- Requirements: drug screen and some hands\-on testing
18qFS3jTDk
Salary Context
This $75K-$95K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Andromeda, 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 in Demand for This Role
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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($85K) sits 60% below the category median. Disclosed range: $75K to $95K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Andromeda AI Hiring
Andromeda has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Lockport, IL, US. Compensation range: $95K - $95K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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.