Interested in this AI/ML Engineer role at Hoffmann Brothers?
Apply Now →About This Role
Hoffmann Brothers is a family-owned, founder-led home services company. Our HVAC, plumbing, and electrical teams take care of homeowners every day, and we invest in the people and technology that make that experience better. Netic, our AI platform, handles a growing share of how customers reach us through voice intake, web booking, and text.
We are looking for an AI Operations Specialist to own the day-to-day rollout of Netic and make sure it works the way our customers and team need it to.
Position Overview
This role has two parts. First, you will project manage our Netic deployment: planning rollouts, coordinating the teams involved, managing the relationship with Netic, and keeping the work on schedule. Second, you will own quality assurance for the platform, testing every release and change across voice, web, and text before it reaches our team or our customers.
Netic connects customer conversations to our booking and dispatch systems, including ServiceTitan. When it works, customers get booked fast and accurately. When it doesn't, it shows up in missed calls, bad bookings, and frustrated CSRs and dispatchers. You will be the person who catches problems early, drives them to resolution, and keeps the deployment moving forward.
AI Deployment Project Management
- Own the Netic deployment plan, including scope, milestones, timelines, dependencies, and owners.
- Coordinate the rollout of new Netic features, workflows, and channel expansions with CX, Dispatch, Operations, IT, and other impacted teams.
- Serve as the day-to-day point of contact with the Netic team, managing the vendor issue queue, escalations, and release schedule.
- Lead recurring deployment check-ins and document decisions, action items, and owners.
- Identify deployment risks and blockers early and escalate them with a recommended solution.
- Prepare impacted teams for each change through release notes, training materials, and go-live communication for CSRs, dispatchers, and leaders.
- Track and report deployment performance, such as booking rate, booking accuracy, handoff rate to live agents, defect volume, and time to resolution.
Netic Quality Assurance
- Conduct thorough testing of Voice Intake, Web Booking, and Text functionality.
- Execute end-to-end testing for all Netic releases and system updates, and approve releases before deployment.
- Validate that Netic bookings flow accurately into ServiceTitan, including job type, business unit, customer, and location records.
- Identify, document, and verify defects, usability concerns, and system inconsistencies.
- Validate that all issues are resolved prior to notifying the internal team of releases.
- Review live interactions (call recordings, transcripts, and text threads) to catch errors, misroutes, and customer friction.
- Audit Netic customer-facing content, including scripts, prompts, automated messages, and confirmations, for accuracy and tone.
Tracking & Reporting
- Maintain the Monday.com board for QA tickets, bugs, feature requests, and deployment tasks.
- Provide a weekly report covering deployment status, outstanding issues, priorities, and resolutions.
Process & Documentation
- Build and maintain test cases, a regression testing checklist, SOPs, and a deployment playbook for future releases.
- Continuously improve testing and deployment processes to increase speed, efficiency, and reliability.
- Collaborate across teams to ensure clear communication and timely resolution of issues.
Requirements
- 2+ years of experience in QA, software testing, software implementation, or project coordination.
- Experience managing a software rollout or implementation with multiple stakeholders and an outside vendor.
- Familiarity with AI systems, conversational or voice-based tools, or booking platforms preferred.
- Experience with ServiceTitan or another field service management platform is a plus.
- Strong working knowledge of Monday.com or similar project management and ticketing systems.
- Ability to translate technical issues into clear updates for frontline teams and leadership.
- Exceptional attention to detail and organizational skills.
- Ability to work independently, manage competing priorities, and follow through without being chased.
- Home services, call center, or customer experience background is a plus.
Benefits
At Hoffmann Brothers, we know our people are the driving force behind our success. That’s why we offer industry-leading benefits, including:
- Health, dental, & vision insurance premiums for the employee and any eligible family members.
- Competitive 401K Retirement program (HB contributes up to 10%!!)
- 15 Paid Time Off Days
- 9 paid holidays
- Referral Program
- Employee Discounts plus Friends & Family Discounts
- We believe in promoting from within and there are many areas to advance in our company
Salary Context
This $46K-$64K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Hoffmann Brothers, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($55K) sits 74% below the category median. Disclosed range: $46K to $64K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Hoffmann Brothers AI Hiring
Hoffmann Brothers has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Maryland Heights, MO, US. Compensation range: $64K - $64K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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