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About This Role
About Us:
Profit Wise Accounting is a full\-service accounting and tax firm dedicated to providing exceptional services in tax planning, payroll, bookkeeping, and business consulting. We focus on client satisfaction and strategic growth, and we are looking for a Marketing Specialist to help enhance our client communications through CRM management and email marketing campaigns. We are a firm that focuses on the small and medium size businesses and the owners of those businesses. At Profit Wise we are here to help Business owners achieve their personal and financial goals through the growth of their business Our firm uses the latest technology to processes to get our work done as efficiently as possible while best serving our clients – and continually improving. We have built a sterling reputation in our community and we take this very seriously – delivering high\-quality, high\-value, and unparalleled customer service – always!
Job Overview:
We have a comprehensive marketing plan in place, We are seeking an experienced Marketing Specialist to implement and manage our CRM (Customer Relationship Management) system and run automated email marketing campaigns. You will be responsible for setting up, optimizing, and maintaining a CRM system tailored to our business needs, ensuring client engagement, retention, and lead nurturing.
Key Responsibilities:
CRM Management:
- Set up and configure a CRM system to track and manage client data.
- Ensure the CRM system is optimized for efficient workflow and client segmentation.
- Train team members on CRM usage and best practices.
- Regularly update and clean the database to ensure data accuracy.
Email Marketing Campaigns:
- Design, build, and execute automated drip email marketing campaigns.
- Develop targeted email campaigns based on client segments, purchase history, and engagement data.
- Analyze campaign performance and make data\-driven decisions to optimize future campaigns.
- Write engaging and relevant email copy that aligns with Profit Wise’s brand and business goals.
Client Engagement \& Nurturing:
- Create strategies to increase client retention through automated follow\-ups, newsletters, and special promotions.
- Segment clients based on their needs (e.g., tax clients, payroll clients) and ensure personalized communication.
- Monitor open rates, click\-through rates, and conversion rates to assess the effectiveness of campaigns.
Qualifications \& Skills:
- Proven experience with CRM systems (HubSpot, Salesforce, Zoho, etc.).
- Strong experience in email marketing and automation tools.
- Excellent written communication and copywriting skills.
- Strong analytical skills to interpret campaign performance data.
- Familiarity with email marketing regulations and best practices (CAN\-SPAM, GDPR compliance).
- Ability to work independently, meet deadlines, and manage multiple projects simultaneously.
Benefits:
- Competitive salary and benefits package.
- Bonus structure.
- 401k Matching
- Health Insurance
- Opportunity for growth and professional development.
- Collaborative team culture.
How to Apply:
Please send your resume and a cover letter detailing your experience with CRM systems and email marketing campaigns to \[Sharon@profitwiseaccounting.biz. Include examples of past campaigns and metrics.
Our Core Values:
- We Are Inspirational — We look at how we conduct business in a very specific way. *Everything that we do is bigger than we are.* We show up to accomplish the things that will make our team more impactful, our client’s businesses stronger, and to chase our potential so that we can achieve our personal, professional, and financial goals.
- We Are Disciplined — We do the things we say we’re going to do. *This dedication to discipline starts at the individual level.*But we don’t just think or talk about discipline, we pursue it through our actions. We train every single day in order to continue learning and growing to reach and then further stretch our potential.
- We Are Accountable — Accountability is central to success. *We’re in the business of holding ourselves and our clients highly accountable to their goals.* True growth doesn’t happen by accident. We believe that extreme accountability yields extraordinary results.
- We Are Aligned — This value is my favorite. *We only hire growth oriented people.* We can’t grow if our team members aren’t serious about their own personal, professional, and financial growth. When we are all in alignment about goals, opportunities, areas of improvement, we can all win. Our teams, our clients, our entire organization is poised to win when we approach our business with an aligned mindset.
- We Are Results — At the end of the day, we are a business. Our clients hire us to grow their businesses, so every service offering we provide is designed to do one thing: deliver results. Being results\-oriented as a core value focuses our thinking and helps us make good decisions.
- We Foster Continuous Learning – We are dedicated to creating a life\-long learning environment whereby every individual is given the encouragement and opportunity to cultivate knowledge, enhance career development and achieve personal growth.
- Client Service – We have a passion for knowing our clients and their needs, for building lasting and mutually beneficial client relationships and for providing comprehensive services that help them create competitive advantages.
Job Types: Full\-time, Contract
Pay: $22\.00 \- $29\.00 per hour
Benefits:
- 401(k) matching
- Health insurance
- Paid time off
Work Location: Hybrid remote in Huntsville, AL 35801
Salary Context
This $45K-$60K range is below the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Profit Wise Accounting, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($53K) sits 68% below the category median. Disclosed range: $45K to $60K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Profit Wise Accounting AI Hiring
Profit Wise Accounting has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Huntsville, AL, US. Compensation range: $60K - $60K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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