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About This Role
Company Overview
GeoPoint is a leading land surveying company renowned for leveraging innovative technology and advanced equipment to deliver exceptional services across diverse projects. With a reputation for setting industry standards, we prioritize innovation, quality, and a collaborative work environment that fosters growth and excellence.
Job Overview
We are seeking an AI Solutions Architect & Automation Lead to turn GeoPoint's AI vision into reliable tools used in day-to-day operations. Reporting directly to the CEO, you will take technical ownership of an evolving platform — starting with a QA/QC workflows GeoPoint’s specialized departments, then expanding into production AI workflows across the business.
This is a build-and-deliver role. You will design, configure, code, test, document, and support working solutions alongside GeoPoint's survey professionals, project managers, and IT team. The initial emphasis is hands-on implementation, not managing an established AI department.
Your First Priorities
First — QA/QC Workflows. Refine the existing pilot into a repeatable workflow for document intake, survey and plat review, cross-document checks, closure evidence, finding resolution, and QA/QC reporting.
Next — Additional Survey Review Types. Extend the proven foundation to boundary, topographic, homebuilding, geospatial, construction as-built & record drawings, and other GeoPoint production reviews, each with its own rules and acceptance criteria.
Then — Business Operations & Production. Expand into accounting AI workflows, HR employee scorecards and rankings, and executive financials and company performance reporting, once the QA/QC foundation is proven. Apply what you learn building the QA/QC product to develop broader production workflows that integrate across the GeoPoint AI Platform.
Growth Potential
Work directly with the CEO to shape the platform and deliver measurable operational improvements. As the program scales, this role may grow into leading a dedicated GeoPoint AI Platform department. The future department structure and leadership responsibilities have not been finalized.
What You Will Own
- Build and improve workflows. Develop reusable Projects, custom GPTs and other AI assistants, and automated workflows using supported tools. Evaluate ChatGPT/OpenAI, Claude/Anthropic, and other options against GeoPoint's requirements; recommend technologies based on test results, security, maintainability, and cost.
- Expand into new domains. Modify and continuously improve production AI workflows as new capabilities come online, and extend into accounting AI workflows, HR employee scorecards and rankings, and executive financials and company performance reporting.
- Build shared data access. Build and maintain a shared data repository/access layer so HR, Accounting, and Executive teams can draw on the data they need under proper governance — supporting GeoPoint's shift toward a company-wide “Data Acquisition” model.
- Make QA/QC results verifiable. Implement source-referenced findings, document coverage tracking, and clear incomplete-check reporting. Integrate tested calculation tools for vector geometry and independent published-call closure checks, using tolerances approved by GeoPoint's survey professionals.
- Establish maintainable foundations. Organize approved rules, reference files, templates, code, and releases in GeoPoint-controlled systems. Maintain version history, repeatable tests, run records, error handling, and operating procedures so the platform can be supported by others.
- Connect systems securely. Partner with GeoPoint's IT Director, Cybersecurity Specialist, Cloud Specialist, and Software Manager on Microsoft 365, SharePoint, Forma, BST11, and other business-system integrations. Apply least-privilege access, secure credential handling, and appropriate retention controls; separate sensitive HR and financial information from general production workflows.
- Lead delivery and adoption. Translate CEO and department requirements into manageable releases. Conduct pilots, train project managers and other employees, troubleshoot issues, and improve workflows based on observed performance.
- Preserve professional accountability. Work with licensed survey professionals and department owners to validate standards and approve results. AI-assisted reviews support, rather than replace, professional judgment and authorized release decisions.
- Measure performance. Track defects identified and missed, false positives, checks completed, review time, reliability, and operating costs. Report progress and risks to the CEO and use the results to guide expansion.
Qualifications & Application
Required Capabilities
- Demonstrated experience delivering software, automation, or enterprise systems, typically five or more years, with recent hands-on implementation of AI workflows used by real teams.
- Strong programming ability in Python and/or JavaScript/TypeScript, plus practical experience with APIs, structured data, Git, and debugging operational workflows.
- Ability to extract and validate information from complex PDFs and spreadsheets, and to distinguish visual interpretation, text extraction, and vector geometry.
- Experience designing task-specific evaluations, repeatable tests, source traceability, and human review checkpoints for accuracy-sensitive work.
- Working knowledge of authentication, access controls, secure data handling, and integration design.
- Ability to translate business requirements into maintainable solutions, communicate limitations clearly, train nontechnical users, and deliver independently with accountability.
Preferred Experience
- Surveying, civil engineering, GIS, CAD, construction, or technical professional services; familiarity with bearings, distances, curve data, and survey deliverables.
- Microsoft 365, SharePoint, Teams, Power Automate, or Power BI; familiarity with Autodesk tools and financial or project-management systems.
- Document retrieval, tool-enabled AI workflows, cloud deployment, SQL, Model Context Protocol (MCP), or similar integration approaches.
A relevant degree or equivalent applied experience is welcome. A surveying license is not required for this technical role; GeoPoint's survey professionals retain responsibility for professional review and acceptance.
The Right Fit
You enjoy building practical tools, investigating why a result is wrong, and working closely with subject-matter experts. You can move from a prototype to a supported workflow without overcomplicating the solution, and you take ownership of documentation, reliability, and user adoption.
Pay: $135,000.00 - $175,000.00 per year
Benefits:
- 401(k)
- 401(k) matching
- Dental insurance
- Health insurance
- Life insurance
- Paid time off
- Vision insurance
Application Question(s):
- How many years of AI do you have?
Work Location: In person
Salary Context
This $135K-$175K range is below the median 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 GeoPoint, 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. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($155K) sits 27% below the category median. Disclosed range: $135K to $175K.
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.
GeoPoint AI Hiring
GeoPoint has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Tampa, FL, US. Compensation range: $175K - $175K.
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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