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About This Role
CPA with Artificial Intelligence & Business Automation skills
- Design, develop, and implement advanced AI systems to automate financial and operational processes.
- Build and manage AI agents, custom chatbots, and intelligent automation workflows.
- Utilize ChatGPT, Grok, and other leading AI platforms at an expert level.
- Develop AI-powered tools for financial underwriting, cash flow analysis, reporting, compliance monitoring, and business intelligence.
- Integrate AI systems with existing business software, databases, spreadsheets, and internal applications.
- Identify repetitive tasks throughout the company and develop automated solutions to reduce or eliminate manual work.
- Build systems capable of extracting, interpreting, and analyzing information from bank statements, financial documents, emails, and spreadsheets.
- Continuously research emerging AI technologies and independently implement solutions that improve productivity.
- Evaluate AI systems for accuracy, data security, reliability, and regulatory compliance.
Programming & Technical Development
- Write and maintain code to automate financial and business operations.
- Demonstrate strong programming knowledge, particularly in Python, SQL, and VBA.
- Develop API integrations connecting AI models with internal and third-party business software.
- Create automated dashboards, internal applications, financial reporting systems, and data-processing tools.
- Build automated workflows using platforms such as Microsoft Power Automate, Zapier, Make, or similar technologies.
- Troubleshoot technical problems independently and develop practical solutions.
- Utilize AI-assisted coding tools to accelerate development while maintaining accuracy and reliability.
Advanced Excel & Microsoft Office
- Demonstrate expert-level proficiency in Microsoft Excel and the entire Microsoft 365 suite.
- Build complex financial models using advanced formulas, XLOOKUP, INDEX/MATCH, pivot tables, Power Query, Power Pivot, macros, and VBA.
- Automate Excel spreadsheets, financial calculations, reconciliations, and recurring reporting.
- Create advanced financial dashboards and interactive business performance reports.
- Demonstrate exceptional proficiency in Microsoft Outlook, Word, PowerPoint, Teams, and other Microsoft applications.
- Utilize AI within Microsoft applications to improve workflow efficiency.
- Process large amounts of financial data quickly and accurately.
Financial Analysis & Accounting
- Perform detailed cash flow analysis to evaluate business financial health and repayment capacity.
- Analyze bank statements, balance sheets, profit and loss statements, financial trends, and business expenses.
- Develop financial forecasts, cash flow projections, budgeting models, and profitability analyses.
- Identify financial inconsistencies, unusual transactions, and potential risks.
- Maintain accurate accounting records, reconciliations, and financial reporting.
- Apply GAAP and relevant accounting standards.
- Evaluate business models, revenue streams, operating expenses, and overall company profitability.
- Use AI-driven financial analysis to improve the speed and accuracy of financial decision-making.
Compliance, Risk Management & Internal Controls
- Maintain a strong understanding of financial regulations, accounting compliance, and business lending requirements.
- Develop automated systems to identify potential compliance violations, financial discrepancies, and operational risks.
- Ensure that AI-powered financial processes comply with applicable regulations and company policies.
- Implement appropriate internal controls, documentation procedures, and financial monitoring systems.
- Support audits, regulatory reporting, and financial risk assessments.
- Protect sensitive financial information through secure technology and responsible AI implementation.
Required Experience & Qualifications
Accounting & Finance
- Active CPA license required.
- Strong experience in financial accounting, cash flow analysis, and financial modeling.
- Exceptional mathematical ability and financial judgment.
- Deep understanding of how businesses operate, generate revenue, manage cash flow, and produce profits.
- Strong knowledge of financial compliance, risk assessment, and internal controls.
AI & Programming
- Expert-level proficiency with ChatGPT, Grok, and multiple AI platforms.
- Demonstrated experience building AI agents, chatbots, and automated workflows.
- Strong coding skills with the ability to independently develop functional programs and automations.
- Experience with APIs, AI integrations, and large-scale data processing.
- Proven ability to use AI to solve complex business problems.
- Ability to evaluate AI-generated output and identify inaccuracies.
Technology & Productivity
- Expert-level Microsoft Excel skills are mandatory.
- Advanced proficiency across Microsoft Office and other business software.
- Extremely fast and accurate typing skills.
- Exceptional computer literacy and technical problem-solving abilities.
- Ability to work quickly across multiple systems without sacrificing accuracy.
- Must be capable of learning new software and emerging technologies independently.
Ideal Candidate
We are looking for someone who is:
- Extremely sharp with numbers: Naturally understands complex financial calculations and identifies errors quickly.
- An AI expert: Goes far beyond basic AI prompting and understands how to create real systems and automations.
- A strong programmer: Can independently write code, integrate software, and develop technical solutions.
- Exceptionally fast: Types quickly, processes information rapidly, and executes tasks efficiently.
- Technologically advanced: Comfortable working with sophisticated software, AI tools, and advanced computing systems.
- Business-minded: Understands financial performance, cash flow, profitability, and the operational realities of running a business.
- Highly analytical: Able to interpret complicated financial information and make sound recommendations.
- Independent and resourceful: Does not require constant supervision to troubleshoot problems or implement solutions.
- Focused on efficiency: Constantly identifies ways to reduce expenses, eliminate unnecessary work, and increase company productivity.
- Detail-oriented: Maintains exceptional accuracy while managing complex financial and technical responsibilities.
Candidate Skills Assessment
Qualified candidates should be prepared to demonstrate their technical and financial expertise through practical assessments involving:
- Advanced Excel financial modeling and automation.
- Cash flow analysis and financial statement interpretation.
- AI agent development and workflow automation.
- Python programming or comparable coding exercises.
- Practical application of ChatGPT, Grok, and AI-assisted business tools.
- Financial compliance and analytical problem-solving.
- Typing speed, accuracy, and technical proficiency.
Why Join Whiteshore Funding?
This is an opportunity to play a critical role in developing the technological and financial infrastructure of a growing financial services company.
You will work on real-world projects combining artificial intelligence, financial analysis, business strategy, programming, and automation.
Our objective is to automate as much of our financial and operational infrastructure as possible while maintaining exceptional accuracy, compliance, and financial oversight.
We are looking for someone who can do much more than manage accounting functions. We want someone capable of building the AI-powered financial systems that help operate and scale an entire company.
If you have exceptional financial intelligence, advanced technical abilities, deep AI expertise, and the drive to continuously improve how businesses operate, we encourage you to apply.
Pay: Up to $80,000.00 per year
Work Location: In person
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 Whiteshore Funding 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
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.
Whiteshore Funding LLC AI Hiring
Whiteshore Funding LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Lexington, KY, US.
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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