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About This Role
Position Information
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The purpose of this classification is to appraise real property to determine appropriate values for tax assessment purposes.
Essential Functions
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The following duties are normal for this position. The omission of specific statements of the duties does not exclude them from the classification if the work is similar, related, or a logical assignment for this classification. Other duties may be required and assigned.
Interprets and applies state and local laws pertaining to property appraisal and assessment while following the annual digest schedule set forth by the Board of Assessors and Chief Appraiser.
Conducts field reviews of residential property, locating and identifying real property, referring to various maps, property indexes, directories, and other documents to determine taxability.
Makes calculations and applies data to property appraisals with consultation and direction from Senior staff and direct Supervisor.
Reviews/interprets property record cards and compares data on cards with actual property in the field, identifying discrepancies and making corrections where needed.
Provides information and assistance to property owners, builders, surveyors, realtors, and other individuals regarding appraisal data, appraised values, property lines, ownership records, appeal rights, and related information.
Explains assessment laws to taxpayers.
Responds to questions, complaints, and correspondence from the public.
Resolves issues involving angry/irate taxpayers.
Researches problems, and initiates problem resolution.
Conducts research of various sources; researches legal records such as deeds, deeds of trust, leases, assessments, and other records; researches returned mail to identify status of taxpayers.
Prepares or completes various forms, reports, correspondence, property record cards, sales reports, improvement reports, sketches/diagrams, and other documents.
Operates a computer to enter, retrieve, and modify data while verifying data accuracy.
Utilizes CAMA system, multi\-listing websites, Excel, Word, multiple GIS applications, Planning and Development customer support system, qPublic, Power Point, Loopnet, Microsoft Teams and Office 365 applications, Property Corrections, Georgia Secretary of State website, and Forsyth County Clerk of Courts deed search.
Operates/utilizes a variety of equipment or tools which may include a motor vehicle, copy machine, scanner, fax machine, calculator, camera, engineering scale, measuring instrument, and telephone.
Maintains a file system of appraisal documents, continuing education materials, returns, appeals, sales reviews, sales ratio studies, and other departmental records.
Communicates with Supervisor, Senior staff, other Forsyth County departments, Board of Equalization, Hearing Officers, Clerk of Courts, Board of Tax Assessors, Certified Public Accountants, property owners, taxpayers, realtors, surveyors, the public, outside agencies, and other individuals as needed to coordinate work activities, review status of work, exchange information, defend appraisal values during appeal hearings, and resolve any other issues.
Maintains a comprehensive, current knowledge and awareness of applicable laws and regulations.
Duties During Digest Schedule:
Returns
Receives returns to the Board of Tax Assessors, contacting taxpayers and scheduling appointments necessary for site visits.
Corrects any mistakes, additions or omissions to the property and sends for review by the Board of Assessors.
Appeals
Receives appeals to the board of assessors.
Contacts taxpayers, tax representatives, or attorneys to schedule any appointments necessary for appeal site visits.
Provides any requested information from the taxpayer within 10 days of receipt of request.
Corrects any mistakes, additions, or omissions during site visits for all appeals and processes information for review by the Board of Assessors to determine the next course of action.
Processes new notices and/or agreements, or forwards appeals to the BOE or Hearing Officer based on findings during the appeal process within the allotted time specified by statute of the Georgia Department of Revenue.
Appeal Hearings
Researches all relevant data to begin case preparation for possible Board of Equalization or Hearing Officer hearings.
Compiles comparable properties into Power Point presentation using information from CAMA system, qPublic, and Multi Listing services.
Provides photos, comparable maps, and data analytics to defend appraised values.
Communicates with the Clerk of Courts about the scheduling and/or rescheduling of all hearings.
Defends appraised values, valuation methods, and uniformity during appeal hearings.
Receives decision rendered by the Board of Equalization or Hearing Officer and processes the appeal according to statute.
Finalizes appeal by writing corrections, if needed, to the Tax Commissioner after resolution to the appeal has been made.
Sales
Compiles a monthly report of all real estate sales to review and update appraisals.
Contacts any real estate agents, taxpayers, neighbors, or closing attorneys to obtain information about the recent sales.
Navigates any multi\-listing websites for information pertaining to sales.
Submits all corrections and updates for data entry into CAMA system after an onsite visit.
Permits
Collects and organizes new construction and addition permits used to value improvements to properties.
Verifies accuracy of map and parcel numbers and addresses, sketches improvements and forms a professional opinion of the quality of construction.
Compiles all property data, structural elements, and land characteristics and submits information for data entry into CAMA system.
Generates reports from CAMA system to ensure information validity.
Revaluation
Plots county\-wide land sales to include acreage, zoning, location, and any other pertinent information.
Determines and utilizes proper land valuation methods based on direction from the Georgia Department of Revenue.
Applies values to assigned neighborhoods, both rural and residential.
Inspects and researches all improved residential property sales to update appraisals.
Uses sale data to determine market values of all residential sales, both vacant and improved, to form an annual residential real\-estate tax\-digest for Forsyth County.
Calculates fair market values and assessment values of real property and approximate amount of property taxes due, maintaining equalization of comparative properties.
Analyzes sales ratio data, generating reports from CAMA system.
Conducts research of various sources and legal records such as deeds, deeds of trust, leases, assessments and other records.
Researches returned mail to identify status of taxpayers.
Consolidates all updates and changes to review for the Board of Tax Assessors.
ADDITIONAL FUNCTIONS
This classification is designated as a safety sensitive position which is subject to random drug and alcohol testing and any other drug testing methods as stated in policies adopted by Forsyth County concerning drug and alcohol.
Performs general/clerical tasks, which may include answering telephone calls, assisting customers at front counter, making copies, sending/receiving faxes, filing documentation, or processing incoming/outgoing mail.
Assists taxpayers in filing for homestead exemption and school tax exemption.
Provides assistance to other employees or departments as needed.
Performs other related duties as required.
Minimum Qualifications
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High school diploma or GED; supplemented by one (1\) year of previous work experience that provides the requisite knowledge, skills, and abilities for this job. Must obtain State of Georgia Appraiser I Certification within one year of employment. Must be 21 years of age. Must possess and maintain a valid Georgia driver’s license.
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 Forsyth County, Georgia, 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.
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.
Forsyth County, Georgia AI Hiring
Forsyth County, Georgia has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Cumming, GA, US.
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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