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About This Role
At Office1, we leverage our "winning triangle" to create an unparalleled company culture. We align our commitment to our customers with our employee goals. We understand that our employees enable our customers' success, and that is why we focus on creating opportunities rooted in our employees' purpose and passions.
Join Office1 and Shape the Future of IT Solutions
At Office1, we align our employees' goals with our commitment to customer success, creating a culture that fosters purpose and passion. Since 1995, we've been delivering innovative technology solutions to SMBs and have become one of the fastest\-growing managed service providers in the Western U.S.
We are seeking an AI Solutions Engineer to join our team. In this role, you'll turn our internal knowledge and manual processes into practical AI\-assisted workflows \- and help bring that capability to our clients. You'll build from the ground up: modernizing our data, developing our internal "company brain," automating the processes that slow our team down, and growing into our go\-to resource for AI workflows.
Build AI that saves real hours \- from the ground up
At Office1, we're putting AI to work on the actual problems our team and our clients face every day. We're hiring our AI Solutions Engineer to lead that build: turning our internal knowledge and manual processes into practical AI\-assisted workflows, then bringing that capability to our clients.
This is a hands\-on, build\-from\-near\-scratch role with real ownership. You'll start by helping modernize our CRM data (a short onboarding project with our Dynamics engineer) so you know our data inside out \- then you'll build our internal "company brain" and automate the processes that eat our team's time.
*Heads up: the CRM work is onboarding, not the job. The job is building AI workflows and automation.*
What you'll do in your first six months
- Help migrate and validate our CRM data alongside our Microsoft Dynamics engineer.
- Build v1 of our internal "company brain" an AI system that answers questions from our own knowledge, with cited sources, measured against a real test set.
- Automate at least two internal workflows with measurable time savings.
- Deliver at least one AI workflow for a client, to an agreed quality bar.
What we're looking for
- You've built a retrieval/RAG system or AI agent over real documents – you understand chunking, retrieval, and keeping a model grounded so it doesn't make things up.
- You can take a vague request and turn it into a clear, testable spec before writing any code.
- You check AI quality with test sets and criteria, not just by eyeballing a few outputs.
- You know LLMs fail in specific ways (hallucination, confident wrong answers, prompt injection) and you design for it.
- You're comfortable in a Microsoft/Azure environment, or you can get there fast.
- Working knowledge of breaking processes into automatable steps, handling sensitive data safely, and keeping AI costs reasonable.
This role is a great fit if...
You like building practical things that save real hours, you want to own an AI capability from the ground up, and you're early in your AI engineering career and eager to grow into our team's AI expert.
It's probably not a fit if...
You want a pure research role, or you'd rather work only on a polished existing platform \- this is a hands\-on, greenfield environment.
Ready to build? Apply with your resume. Be ready to discuss an AI workflow or system you've built and what you learned from it.
Diversity
Office1 believes we work more productively, and our jobs are more enjoyable, when our team includes members with a diversity of backgrounds and life experiences. We take all reasonable steps to seek out candidates with diverse experience and ensure our work environment is welcoming and respectful for everyone on our team.
What We Offer
- Paid time off, holidays, and vacation
- Comprehensive medical, dental, and vision coverage with generous contributions
- Supplemental benefits (e.g., STD/LTD, life, EAP)
- 401K matching
- Onsite parking, gym, showers, and activity rooms
Benefits
- 401(k)
- 401(k) matching
- Dental insurance
- Health insurance
- Life insurance
- Paid time off
- Vision insurance
Location
- Las Vegas, NV 89101 (Required)
- This is an onsite position in Las Vegas, NV. There is no hybrid or remote work available.
Benefits:
- 401(k)
- Dental insurance
- Health insurance
- Life insurance
- Paid time off
- Vision insurance
Application Question(s):
- This is an on\-site, Monday\-Friday role. Does this work with you?
Location:
- Las Vegas, NV 89101 (Required)
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At OFFICE1, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
OFFICE1 AI Hiring
OFFICE1 has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Las Vegas, NV, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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