Interested in this AI/ML Engineer role at Kammeier Property Group?
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About This Role
About Us
Kammeier Property Group is a high\-performing real estate team serving St. Charles, Lincoln, and Warren Counties. We’re committed to top\-tier client service, community connection, and innovative marketing. To support our continued growth, we’re seeking a creative and detail\-driven Digital Marketing Specialist to lead our marketing initiatives and help us stand out in the local real estate market. This role will have a HUGE emphasis on Ai content and research. Our goal is becoming one of the most AI\-visible real estate teams in our area.
Role Summary \& Position Overview
Kammeier Property Group is seeking a highly motivated AI Content \& Marketing Specialist to lead our digital marketing, SEO, and content strategy. This role is ideal for someone who enjoys research, understands search behavior, and can transform market insights into engaging content across multiple platforms. The primary objective is to position Kammeier Property Group as the leading real estate resource throughout St. Charles, Lincoln, Warren County, and the surrounding St. Louis market.
The Marketing Specialist will manage the team’s marketing calendar, digital presence, and content creation. This role touches every part of our brand—from social media and email newsletters to listing marketing, website updates, and event promotion. If you’re passionate about real estate, love creating engaging content, and thrive on keeping systems organized, this is the role for you.
Primary Mission
The most important responsibility of this position is researching the questions, concerns, and trends that buyers and sellers are actively searching online, then developing educational content that answers those questions across every marketing channel.
Key Responsibilities
Content \& Campaigns
- Plan, design, and schedule social media posts across multiple platforms.
- Write blog posts, community highlights, and market updates for the website.
- Build monthly newsletters and email drip campaigns for our database.
- Collaborate with agents on video ideas, reels, and story content.
- Research consumer search behavior across Google, Google AI, ChatGPT, Perplexity, Reddit, YouTube, TikTok, Facebook Groups, Zillow, Realtor.com, Google Trends, AnswerThePublic, and other relevant platforms.
- Maintain an ongoing database of high\-value buyer and seller questions and emerging content opportunities.
- Manage Facebook, Instagram, LinkedIn, YouTube, TikTok, Threads, and Google Business Profile.
- Monitor and improve website SEO including keyword optimization, internal linking, metadata, image optimization, local SEO, and AI search visibility.
- Publish weekly Google Business Profile updates and manage reviews, photos, Q\&A, and business information.
- Research local housing statistics, economic development, schools, builders, neighborhoods, and community news to create timely market content.
- Track analytics and prepare monthly performance reports covering website traffic, SEO rankings, Google Business Profile, social media, and lead generation.
- Maintain consistent branding across all marketing materials.
Listing Marketing
- Create and distribute listing flyers, brochures, social posts, and Just Listed/Just Sold campaigns.
- Coordinate with photographers, stagers, and vendors for listing preparation.
- Post, update, and maintain listing content on the website and syndication platforms.
- Input and manage current listing inventory
- Create, Run, and Manage online paid property advertising
Database \& CRM Marketing
- Maintain accuracy of database/CRM contact lists.
- Execute marketing touches (birthday/holiday campaigns, client event invites, follow\-up sequences).
- Track and analyze engagement metrics to improve campaigns.
Success Metrics (KPI's)
- Develop a minimum of 100 researched content ideas each month.
- Publish 4–8 SEO blogs per month.
- Produce consistent short\-form and long\-form video content.
- Maintain an active posting schedule across all social media platforms.
- Publish weekly Google Business Profile posts.
- Increase website traffic, keyword rankings, and local search visibility.
- Grow engagement, audience size, and lead generation through organic marketing.
- Continuously improve Kammeier Property Group's visibility within AI\-powered search platforms and standard browsers.
Ideal Candidate
- 1–3 years in marketing (real estate or small business preferred, but not required ).
- Strong writing skills with attention to detail and creativity.
- Proficiency with Canva, Mailchimp/Constant Contact (or similar), and social scheduling tools.
- Familiarity with CRMs (e.g., FollowUpBoss/KVCore) and website platforms (WordPress, Squarespace, etc.).
- Organized, proactive, and able to juggle multiple projects at once.
- Experience with SEO, digital marketing, content marketing, or social media management.
- Strong research and analytical abilities.
- Familiarity with Google Analytics, Google Business Profile, WordPress or similar CMS, and major social media platforms.
- Experience with AI tools such as ChatGPT, Gemini, Claude, or Perplexity is preferred.
What We Offer
- A collaborative, creative, and supportive team culture.
- Opportunity to grow with one of the top teams in the region.
- semi\-Flexible work environment (office \+ with some flexibility).
- Competitive salary$18\.00\-$22\.00 (DOE) \+ performance\-based incentives.
- Professional development opportunities.
- Monthly team outings/bonding
- Yearly team travel for training
Job Type: Full\-time
Pay: $18\.00 \- $22\.00 per hour
Benefits:
- Flexible schedule
- Paid time off
- Professional development assistance
- Referral program
Work Location: In person
Salary Context
This $37K-$45K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Kammeier Property Group, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($41K) sits 81% below the category median. Disclosed range: $37K to $45K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Kammeier Property Group AI Hiring
Kammeier Property Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in O'Fallon, MO, US. Compensation range: $45K - $45K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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