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About This Role
About Hernandez
Hernandez Development \& Construction is a Florida real estate and construction platform working across industrial, commercial, and self\-storage assets. The company’s work includes development, construction, asset management, investor relations, HR, IT, accounting and finance, marketing, and business operations.
The Opportunity
We are looking for an early\-career Executive Projects and Operations Coordinator to work directly with a senior executive involved in several parts of the business. This is not a traditional administrative or calendar\-management role.
The position is for a sharp, curious person who wants to learn how a growing real estate and construction company operates from the inside. You will help move priority projects forward, research unfamiliar topics, organize information, prepare useful business materials, and support practical improvements across different teams.
You will not be expected to know everything on day one. You will be expected to learn quickly, use the resources available, think through problems, and bring back clear work that helps leadership make decisions and keep momentum.
What You Will Do
- Support special projects and priority initiatives across marketing, HR, IT, finance, asset management, and business operations.
- Take incomplete questions or requests, identify what information is missing, research reliable sources, and prepare a concise summary with recommended next steps.
- Build and maintain project trackers, research briefs, spreadsheets, presentations, reference materials, and simple dashboards.
- Help organize business information, project documents, shared files, and operating resources so teams can find what they need quickly.
- Use approved AI tools to improve research, first drafts, information organization, and repeatable work, while reviewing outputs for accuracy.
- Learn the company’s technology and business processes over time, then help identify simple ways to improve clarity, follow\-through, and efficiency.
- Coordinate with team members and outside partners to gather information, clarify open items, and keep defined projects moving.
- Handle confidential business and employee information with maturity and discretion.
What You Will Learn
This role provides direct exposure to executive decision\-making and to the different parts of a real estate and construction company. You will gain practical experience in business operations, project coordination, marketing support, internal processes, technology tools, and strategic communication.
The role is designed for someone who wants to grow into larger responsibilities. Your first priority is to become reliable in project support, research, and follow\-through. As you build trust and show strong judgment, you will take on more complex work and develop deeper experience in the business areas that match your strengths.
What We Are Looking For
- Bachelor’s degree required, preferably in business, finance, communications, operations, marketing, information systems, or a related field.
- A strong new graduate or a candidate with up to three years of experience through work, internships, analyst roles, project coordination, consulting, operations, or comparable experience.
- Strong written communication, organization, attention to detail, and ability to turn information into a clear summary.
- Comfort with Microsoft 365, including Excel, PowerPoint, Word, Outlook, shared files, and online research.
- Curiosity about AI and technology. Familiarity with tools such as ChatGPT, Claude, Gemini, Microsoft Copilot, or similar tools is helpful, but prior technical expertise is not required.
- Ability to work through ambiguity, ask specific questions, identify practical next steps, and take ownership of assigned work.
- Good judgment about confidentiality, accuracy, and when a decision requires executive or functional\-owner approval.
- Ability to work full\-time, in person, in Fort Lauderdale.
This Role Is a Good Fit If You
- Like learning how a business works rather than staying in one narrow job lane.
- Enjoy taking a messy question and making it easier to understand and act on.
- Are comfortable learning new tools and using technology to work faster and more accurately.
- Want direct exposure to leadership, practical projects, and several areas of a business.
- Care about doing high\-quality work without needing someone to repeat every instruction.
Join us as we drive operational excellence while harnessing the power of AI innovation! This paid position offers an exciting opportunity to contribute to impactful projects within a forward\-thinking company committed to growth and success.
Pay: $65,000\.00 \- $70,000\.00 per year
Benefits:
- 401(k)
- 401(k) matching
- Dental insurance
- Health insurance
- Health savings account
- Life insurance
- Paid time off
- Professional development assistance
- Vision insurance
Work Location: In person
Salary Context
This $65K-$70K 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 Hernandez Construction 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. This role's midpoint ($67K) sits 69% below the category median. Disclosed range: $65K to $70K.
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.
Hernandez Construction LLC AI Hiring
Hernandez Construction LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Fort Lauderdale, FL, US. Compensation range: $70K - $70K.
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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