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About This Role
Main Duties & Responsibilities: Primary Job Role
Design and develop AI-powered applications and workflows to improve efficiency across departments.
Integrate AI solutions with enterprise platforms (e.g., Microsoft Dynamics 365, Azure services).
Collaborate with business units to identify opportunities for AI-driven automation.
Monitor and optimize AI solutions for performance, scalability, and security.
Support AI and analytics initiatives by enabling advanced data capabilities.
Stay informed on emerging AI technologies and contribute ideas for innovation.
Deliver training and enablement sessions for business users on Microsoft Copilot and other AI tools.
Performs other duties as assigned. Essential Job Competencies
Safety: fully supports, encourages and follows safe work behavior, and considers safety of paramount importance in the workplace.
Job Specific Skills: Demonstrates the required depth and mastery of knowledge and skill associated with the job as identified in the Learning Management System (LMS) and other applicable sources. Demonstrates the effective application of the job specific knowledge, skills and ability required to resolve job related challenges in the workplace. Refer to job specific training requirements for the job identified in the LMS.
Communication: Uses active listening skills, conveying information with the appropriate medium that is clear and easily understood. Uses feedback to verify effective and accurate communication has occurred. Ensures that others having a need to know are kept informed about developments, progress, problems and plans through consistent, effective communication. Avoids surprises.
Analytical: Uses a logical, systematic, sequential approach breaking down complex tasks into its component parts and considering each part in detail. Compares alternatives, evaluating the costs, benefits, risks, and chances for success, in making decisions. Identifies patterns and trends to determine root cause. Generates a range of creative solutions, choosing the most appropriate option.
Adaptability: Adapts to changing business needs, conditions, and work responsibilities. Adapts approach, goals, and methods to achieve solutions and results in dynamic situations. Recovers quickly from setbacks, and finds alternative ways to reach goals or targets. Open to different and new ways of doing things; willing to modify one’s preferred way of doing things.
Customer Oriented: Demonstrates concern for satisfying external and/or internal customers. Responsive, quickly and effectively addresses customer concerns or problems. Assures customers he/she is willing to work with them to meet their needs. Presents a cheerful, positive manner with customers.
Results Oriented: Has a strong sense of urgency and commitment to achieve desired results in the face of obstacles and frustrations. Remains focused, does not get bogged down in unnecessary detail. Identifies and utilizes the most efficient methods to successfully achieve tasks on time despite unforeseen events. Driven, maintains a sense of urgency and adjusts schedules as needed to achieve without sacrificing quality of work.
Coaching and Mentoring: Inspires and empowers team members to excel, providing timely productive feedback and guidance to stretch beyond their comfort levels to achieve specific, measurable and challenging goals. Builds productive relationships with team members, conveying confidence and expressing genuine appreciation for team member contributions. Explains to team members the importance of the work they do.
Information Gathering: Checks all appropriate sources in identifying the information needed for analysis and decision making. Uses skillful methods in obtaining high quality relevant information. Checks work to ensure accuracy and completeness. Compares observations or finished work to what is expected to find inconsistencies. Clearly documents sources, and organizes the information according to the research needs.
Planning and Organizing: Creates realistic schedules and follows them. Evaluates progress against schedule and goal. Identifies the sequence of tasks and the resources needed to achieve a goal, and prioritizes key action steps. Anticipates the impacts / risks of actions. Seeks and uses others’ input about critical actions, timelines, sequencing, scope, methodology, expected outcomes, and priorities. Adjusts plans based on input. Company Overview:
In 1962, Ring Power Corporation became a full-line Caterpillar dealer in North Florida, and later expanded its authorized territory to include Central Florida.
Today, Ring Power Corporate headquarters in St. Augustine oversees the operations of 18 branch locations throughout the state of Florida, including large regional facilities in Tampa and Orlando and crane and forklift sales and service facilities in Pompano. Ring Power also has five facilities outside of Florida - The Carolinas, Georgia, & Texas- to serve the needs of other specialized industries and customers.
Ring Power Corporation has become one of the largest Caterpillar dealers in the Southeastern United States through dedication to the mission of customer service. In order to preserve the "Customers First" reputation that Ring Power was built on, we continually train our employees and work closely with our customers to assure complete satisfaction, especially after the sale.
Currently, more than 2,700 employees at 20+ locations throughout Florida and the United States work hard to provide the highest quality construction equipment, backed by responsive, professional service and support at every level. Qualifications: Education and Experience
Formal Education: Bachelor’s Degree in a related area of study.
Experience: 1-2 years Required / Credentials
Familiarity with AI/ML frameworks and tools (e.g., Azure AI, OpenAI APIs, Microsoft Copilot).
Strong analytical, problem-solving, and communication skills.
Basic proficiency in programming languages such as Python, C#, or JavaScript.
Understanding of cloud platforms (Azure preferred) and API integration. Not Required but Highly Desired Criteria
Experience with Microsoft Dynamics and enterprise data environments.
Knowledge of prompt engineering and generative AI models.
Exposure to data analytics or business intelligence tools. Updating of Knowledge
Job requirements frequently change requiring re-training once or more per year. Responsibility for Change, Innovation, Overall Improvement, and/or Effectiveness
Problem Solving: Job requires complex problem-solving ability with little guidance. Independent research and analysis are sometimes required to develop solutions.
Creativity: Job requires extensive creativity and a constant need to find new and improved solutions to be effective.
Autonomy: Job is regularly monitored to determine outcomes. Assignments allow some discretion in decision making and setting priorities within well-established parameters. Working Environment
Stress Load: Regular exposure to these stresses (20-80% of the time).
Workload Fluctuation: Job frequently involves changes in priorities, complexity and/or quantity of work.
Work Schedule: Work is typically performed during normal business hours with occasional requirements to work nights, holidays or weekends. Organizational Impact
A person's performance in this job has considerable immediate impact on expense, efficiencies or achievement of overall department objectives. Supervisory Responsibility
Job has no subordinates / no supervisory responsibility (may work independently or as a member of a team) Physical Demands "NA": Not Applicable "O": Occasionally - (up to 3 hours/day) "F": Frequently - (3-6 hours/day) "C": Constantly - (6-8 hours/day) O: Climbing
O: Balancing
O: Stooping
O: Kneeling
O: Crouching
O: Crawling
O: Reaching
F: Standing
C: Sitting
F: Walking
O: Feeling
O: Fingering
O: Grasping
C: Repetitive Motion
F: Talking
C: Hearing
O: Pushing - Up to 25 lbs.
O: Pulling - Up to 25 lbs.
O: Lifting - Up to 25 lbs.
N/A: Pushing - 26-50 lbs
N/A: Pulling - 26-50 lbs.
N/A: Lifting - 26-50 lbs.
N/A: Pushing - Over 50 lbs.
N/A: Pulling - Over 50 lbs.
N/A: Lifting - Over 50 lbs.
Environmental Conditions "NA": Not Applicable "O": Occasionally - (up to 3 hours/day) "F": Frequently - (3-6 hours/day) "C": Constantly - (6-8 hours/day) N/A: Inside conditions: Protection from weather but not necessarily from temperature change.
N/A: Outside environmental conditions: No effective protection from weather.
N/A: Extreme cold: Temperatures below 32 degrees for periods of more than one hour.
N/A: Extreme heat: Temperatures above 100 degrees for periods of more than one hour.
N/A: Noise: sufficient noise to cause the worker to shout to be heard above the ambient noise level N/A: Vibration: Exposure to oscillating movements of the extremities of whole body.
N/A: Hazards: Includes a variety of physical conditions (i.e. proximity to moving mechanical parts, electrical current, working in high places, exposure to heat and/or chemicals.
N/A: Atmospheric conditions: One or more of the following conditions that affect the respiratory system or the skin: Fumes, odors, dust, mists, gases or poor ventilation.
N/A: Oils: There is air and/or skin exposure to oils and other cutting fluids.
N/A: Air particulates / contaminants: the worker is required to wear respirator.
RING POWER CORPORATION, INC. IS AN EQUAL OPPORTUNITY EMPLOYER
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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Ring Power Corporation, 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 $212,000 based on 11,866 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 $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Ring Power Corporation AI Hiring
Ring Power Corporation has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Saint Augustine, FL, US.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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