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Looking to jumpstart your career in AI development? ExamWorks is looking for an AI Prompt & Workflow Specialist to join the team!
This is a 100% remote role, standard business hours Monday - Friday in your local time zone.
The AI Prompt & Workflow Specialist designs, tests, implements, and maintains AI prompts, agents, and workflow solutions that support business operations. Working closely with Business Analysts, Product, Operations, and Development teams, this role translates business requirements into effective AI-enabled processes and automation solutions. The position is responsible for optimizing prompts and workflows, evaluating results, troubleshooting issues, and ensuring AI solutions deliver accurate, reliable, and scalable business outcomes.
ESSENTIAL JOB FUNCTIONS
- Design, develop, test, and continuously refine AI prompts, agents, and automation workflows.
- Translate business requirements into AI-enabled solutions, including workflow design, prompts, inputs, outputs, instructions, and expected results.
- Design and maintain multi-agent workflows where outputs are evaluated, combined, summarized, or routed to support business processes.
- Develop test scenarios; evaluate AI outputs for accuracy, consistency, completeness, and quality; and troubleshoot and optimize agent performance based on testing and production results.
- Maintain and enhance existing AI agents and workflows as business requirements, models, and technologies evolve.
- Partner with Business Analysts, business users, and other stakeholders to understand processes, identify automation opportunities, and validate AI-driven solutions.
- Provide guidance and support to business users developing or maintaining AI agents and workflows.
- Collaborate with Development teams to integrate AI workflows into the system and other business applications.
- Establish and maintain best practices for prompt development, testing, documentation, governance, and ongoing support.
- Perform other job-related duties consistent with the nature and level of the position.
QUALIFICATIONS
- Bachelor's degree in Business Administration, Information Systems, Computer Science, Data Analytics, Communications, or a related field preferred.
- Demonstrated hands-on experience designing, building, testing, or optimizing generative AI prompts, agents, or AI-driven workflows.
- Experience may come from professional work, internships, academic projects, certifications, or other applied AI projects.
- Must be highly detail-oriented with the ability to evaluate outputs for accuracy, consistency, completeness, and quality.
- Must demonstrate strong analytical, problem-solving, and critical-thinking skills.
- Must demonstrate effective written and verbal communication skills, including the ability to document processes and explain technical concepts to business users.
- Ability to manage multiple priorities, adapt to changing business needs, and work effectively in a fast-paced environment.
- Ability to learn and apply new technologies, AI platforms, tools, and business processes quickly.
- Ability to analyze business processes and translate requirements into structured AI prompts, workflows, and testing scenarios.
- Must demonstrate collaboration and teamwork skills while building effective relationships with business and technical stakeholders.
- Ability to troubleshoot issues, perform testing, and refine solutions through experimentation and iterative improvement.
- Ability to maintain confidentiality and safeguard sensitive business and client information.
WHO WE ARE
ExamWorks is a leading provider of innovative healthcare services including independent medical examinations, peer reviews, bill reviews, Medicare compliance, case management, record retrieval, document management and related services. Our clients include property and casualty insurance carriers, law firms, third-party claim administrators and government agencies that use independent services to confirm the veracity of claims by sick or injured individuals under automotive, disability, liability and workers' compensation insurance coverages.
Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, pregnancy, genetic information, disability, status as a protected veteran, or any other protected category under applicable federal, state, and local laws.
Equal Opportunity Employer - Minorities/Females/Disabled/Veterans
ExamWorks offers a fast-paced team atmosphere with competitive benefits (medical, vision, dental), paid time off, and 401k.
Salary Context
This $95K-$105K range is in the lower quartile 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 ExamWorks, 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 in Demand for This Role
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. This role's midpoint ($100K) sits 53% below the category median. Disclosed range: $95K to $105K.
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.
ExamWorks AI Hiring
ExamWorks has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Atlanta, GA, US. Compensation range: $105K - $105K.
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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