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About This Role
The Engagement Stewards at Fitness Interface LLC/RetainTeamGrant.com are looking for other stewards much like ourselves.
This is not quite a sales job.
It is not quite a consulting job.
It is not quite a product job.
It is not quite an engineering job.
And it is definitely not a conventional recruiting job.
It is an Engagement Stewardship job.
Fitness Interface LLC is building a retainer\-based professional AI engineering network around a simple proposition:
We Do AI Right—Because We Do IT Right™—together with you—right on the streets where we live.
Our engineers may sit anywhere in the world.
Our clients need someone they can trust—and reach out to— right in their own back yards.
Between the two stands an Agile AI Engagement Steward, perhaps the steward that YOU were always meant to be.
Someone who can walk into a room with an entrepreneur, C\-Suite member, or small\-medium business owner, listen carefully enough to understand what really hurts, help turn a fuzzy business problem into something an engineering team can actually build, assemble the right people around the work, and then remain present while everybody figures it out together.
All while working within a 100% retainer\-based ecosystem.
Someone who can be the leader in the Zoom.
THE PERSON WE’RE LOOKING FOR
You are unusually interdisciplinary.
You may have written production software yourself.
You may have designed full\-stack systems involving modern web applications, APIs, databases, cloud infrastructure, AI models, RAG pipelines, agents, automation, or other emerging technologies.
You don’t have to be the engineer writing every line of code today.
But you should understand engineering deeply enough that excellent engineers recognize you understand what they are doing—and that you know when to get out of their way.
You can also communicate.
Really communicate.
You can sit across from a founder who doesn’t know a vector database from a carburetor and help them understand why an architectural decision matters without making them feel undervalued.
You can write.
You can present.
You can market an idea on all forms of media.
You can tell a story.
You can make complicated things understandable without making them simplistic.
And when there is business to be won, you are comfortable winning it. ABC123 style.
Not through pressure.
Through trust, curiosity, competence, imagination, and demonstrated value.
YOU CAN MOVE BETWEEN WORLDS.
On Monday morning, you might be helping an entrepreneur discover what they actually need.
Monday afternoon, you might be working with engineers to turn that discovery into a practical first engagement.
Tuesday, you might help shape an architecture or challenge an assumption.
Wednesday, you might remove a roadblock between a client and the team.
Thursday, you might recognize an unmet need nobody has articulated yet.
Friday, you might help turn that need into the next retainer.
And somewhere in between, you may write the email, shape the pitch, make the introduction, facilitate the meeting, sketch the workflow, review the prototype, calm the room, ask the uncomfortable questions, celebrate the wins, and make sure everybody knows what happens next.
That is the job. Because that is the job our Chief Engagement Steward, Grant, does now.
YOU PROBABLY HAVE SOME SCARS.
We are particularly interested in people who have actually built things.
Business counts.
Software counts.
Products count.
Communities count.
Campaigns count.
Teams count.
Because people count most of all.
Things that failed spectacularly count too—if you learned something worth carrying forward.
We value demonstrated capability more than pedigree.
A long list of certifications will not impress us nearly as much as evidence that you can encounter an unfamiliar problem, learn quickly, reason carefully, work with good people, and get something useful across the finish line.
YOU UNDERSTAND AI—WITHOUT WORSHIPPING IT.
AI is extraordinary.
It is also just software at the end of the day.
It lives inside systems built by humans, connected to other systems built by humans, serving people with real needs, constraints, risks, hopes, budgets, and deadlines.
So we are looking for someone who understands both the possibilities of modern AI engineering and the durable disciplines of good information technology that have made AI possible in the first place.
Architecture matters.
Security matters.
Data matters.
Testing matters.
Observability matters.
Maintainability matters.
Privacy matters.
User experience matters.
Cost matters.
Because customer\-specific OKRs matter first.
And knowing WHY we are building the thing in the first place matters most of all.
You should be comfortable reasoning about modern full\-stack AI systems even when somebody else ultimately implements them.
YOU UNDERSTAND AGILE AS MINDSET AND WAY OF THINKING… not as a mere datastore to dust off when you need your certification renewed.
We are not looking for a ceremony cop or walking textbook.
We are looking for someone who understands how people discover useful things together.
Start with what matters.
Deliver something valuable.
Look at what happened.
Learn.
Adapt.
Keep moving.
Make commitments responsibly.
Change direction when reality teaches us something.
Help people communicate before process becomes a substitute for communication.
Because the framework is secondary.
The stewardship is ALWAYS primary.
YOU HAVE HEALTHY COMMERCIAL INSTINCTS.
You understand that meaningful work has to be funded.
You are comfortable developing relationships, discovering opportunities, discussing value, helping scope engagements, and asking clients to make commitments.
You understand that selling excellent professional services should feel less like persuasion and more like jointly discovering whether there is something worthwhile for us to accomplish together.
You care about the engagement after the signature just as much as the engagement before it.
Because the real objective is not winning a project.
It is creating enough continuing value that good clients want us around.
And most importantly—you want to be the Fitness Interface “face character” in every room you enter.
YOU ARE A STEWARD.
This may be the most important qualification of all.
You take responsibility without needing ownership of everything.
You notice people.
You listen.
You follow through.
You protect trust.
You can carry authority without constantly displaying it.
You can disagree without humiliating somebody.
You can lead without making yourself the center of the enterprise.
You know when to speak.
You know when to ask.
You know when to remain silent up and let the engineer engineer.
And when the room gets uncertain, complicated, political, technical, emotional, or simply weird…
You can still stand on your own two feet and help everybody find the next useful move—in the Zoom or not.
THINGS WE WOULD LOVE TO DISCOVER IN YOUR BACKGROUND
Experience across several of these areas would be useful:
- Full\-stack software or AI engineering
- AI\-assisted application development
- Product discovery and solution design
- Agile/Scrum/Lean delivery with an AI twist
- Technical consulting
- Entrepreneurship
- Professional services
- Client engagement
- Business development
- Marketing or communications
- Public speaking or facilitation
- Team leadership
- Account stewardship
- Systems thinking
- Workflow and process improvement
We are not expecting one candidate to have held thirteen corresponding job titles.
We are looking for evidence that your curiosity and career have repeatedly carried you across boundaries into the integrated, wholistic professional you now are.
THE TEST ISN’T WHETHER YOU KNOW EVERYTHING.
You won’t.
Neither do we.
The question is what you do when none of us knows yet.
Can you ask the question that changes the conversation?
Can you recognize an assumption masquerading as a requirement?
Can you learn something unfamiliar quickly?
Can you bring the right person into the conversation?
Can you distinguish technological possibility from business usefulness?
Can you help a group make a responsible decision with incomplete information?
Can you keep moving without pretending uncertainty does not exist?
Can you keep groking? And be satisfied with never completely grokking?
Because that matters here. And that one K makes all the difference in the world.
HOW WE WORK
We are developing our model in Las Vegas while building a professional network capable of serving clients anywhere there is a metro area supporting sustainable businesses that could benefit from AI and the Agentic Technology Ecology.
Great engineering talent can sit wherever great engineering talent happens to live.
Engineers can sit anywhere—and commute nowhere at all.
Our Engagement Stewards provide the human continuity around that distributed capability: building relationships, understanding needs, bringing the right people together, supporting execution, helping successful engagements grow, and the engineering team member under your care to stay retained.
Camaraderie grows in the trenches.
TRUST blooms in the Zoom.
And just like the Fast and Furious folks, we may be very individualistic, but we sink or swim as an Agile team.
THE CONVERSATION
If this sounds unusually familiar to you, we would like to meet you.
Please don’t prepare for a certification exam.
We are not particularly interested in discovering how many technical buzzwords you can recall on command.
We would rather give you a realistic business situation and spend some time figuring it out together.
We want to see how you think.
How you listen.
How you question.
How you explain.
How you navigate tradeoffs.
How you react when the situation changes.
How you bring people with you.
And whether, when all of us temporarily lose the plot—you might be the person in the room who helps the Zoom find it again.
We aren’t looking for perfection.
We’re looking for another human being willing and able to stand with us, help good people do meaningful work, and keep learning along the way.
Keep Groking. Keep the Faith.
Steward in the loop.
So that Mercy may abound.
Right on the streets where we live.
Fitness Interface LLC
RetainTeamGrant.com
*We Do AI Right—Because We Do IT Right™—together with you.*
If this resonates with you, apply for this job—submit your resume today.
Pay: From $40,000\.00 per year
Work Location: In person
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 Fitness Interface 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
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.
Fitness Interface LLC AI Hiring
Fitness Interface LLC has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Las Vegas, NV, US, Remote, US.
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
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