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About This Role
AI Specialist & Office ManagerCompany: J&J Brothers, LLC
Location: Meriden, Connecticut
Position: Full-Time | In-OfficeSchedule: M-F 8AM-4:30PM Position Overview J&J Brothers, LLC is seeking a highly motivated, tech-savvy AI Specialist & Office Manager to help modernize our business operations, improve efficiency, and implement artificial intelligence (AI) solutions across our organization. The ideal candidate will have a strong understanding of AI technology, business automation, office management, and administrative operations. We are looking for someone who can identify opportunities to streamline workflows, eliminate repetitive tasks, and introduce innovative technology to improve productivity. This position combines traditional office management responsibilities with a strong emphasis on AI integration, automation, and operational improvements. Key ResponsibilitiesResponsibilities include but are not limited to: Artificial Intelligence & Automation* Research, evaluate, and implement AI tools to improve business operations.
- Develop automated workflows for administrative tasks, reporting, scheduling, and document management.
- Integrate AI solutions into existing business software and processes.
- Identify opportunities to reduce manual work and improve employee productivity.
- Utilize tools such as ChatGPT, Microsoft Copilot, Power Automate, and other AI platforms.
- Develop AI-assisted reporting, dashboards, and data analysis tools.
- Train employees on AI tools and best practices.
- Work with management to develop and execute a company-wide AI implementation strategy.
Office Management & Administration* Oversee daily office operations and administrative functions.
- Coordinate communication between management, employees, vendors, and customers.
- Manage office procedures, records, and digital filing systems.
- Assist with project tracking, scheduling, and operational reporting.
- Support accounting, payroll, human resources, and project management teams.
- Improve internal communication and workflow efficiency.
- Maintain organized and efficient office systems.
Business Process Improvement* Evaluate existing business processes and recommend improvements.
- Develop standardized operating procedures (SOPs).
- Implement digital solutions for document processing, invoicing, reporting, and project tracking.
- Assist management with operational analysis and strategic initiatives.
- Track and measure the effectiveness of newly implemented technology.
- Identify opportunities for cost savings through automation.
Qualifications* Experience with AI applications, business automation, or technology implementation.
- Strong organizational, communication, and problem-solving skills.
- Proficiency in Microsoft Office, particularly Excel, Outlook, and Microsoft 365.
- Familiarity with ChatGPT, Microsoft Copilot, Power Automate, Zapier, or similar platforms.
- Experience in office administration, operations, or project coordination.
- Ability to independently research, recommend, and implement new technologies.
- Strong analytical skills and attention to detail.
- Ability to handle confidential business information professionally.
- Experience in construction, contracting, property management, or related industries is a plus.
Preferred Qualifications* Experience building AI-powered workflows and business automations.
- Knowledge of QuickBooks, accounting software, or ERP systems.
- Familiarity with AI integrations, APIs, and database management.
- Experience creating automated reports and management dashboards.
- Previous experience managing administrative teams or office operations.
What We're Looking For We are seeking a forward-thinking individual who understands how AI can transform traditional business operations. The successful candidate will not simply manage an office but will actively introduce new technologies, automate inefficient processes, and help position our organization for future growth. We want someone who takes initiative, thinks creatively, and can turn ideas into practical, measurable improvements. Compensation & Benefits* Competitive salary based on experience and qualifications.
- Full-time position with opportunities for professional growth.
- Opportunity to lead AI and technology initiatives across multiple businesses.
- Collaborative work environment with direct interaction with senior management.
- Benefits to be discussed during the interview process.
Join our team and help shape the future of our business through artificial intelligence, automation, and innovation. How to Apply Interested candidates should submit their resume along with a brief description of their experience implementing AI tools, automation, or technology improvements in a business environment.
Salary Context
This $75K-$85K 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 The Carabetta Companies, 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 $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 ($80K) sits 62% below the category median. Disclosed range: $75K to $85K.
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.
The Carabetta Companies AI Hiring
The Carabetta Companies has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Meriden, CT, US. Compensation range: $85K - $85K.
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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