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About This Role
DEPARTMENT: Data Insights and Innovation
JOB TITLE: AI QA Engineer
JOB CODE: AIQAE
REPORTS TO: Manager, AI and Intelligent Decisioning
FLSA STATUS: Exempt
EMPLOYMENT TYPE: Full\-Time
JOB PURPOSE:
This role at Arbitration Forums is as unique as it is rewarding because of the AF IPAAL Values (Integrity, Passion, Accountability, Achievement, Leadership) and TRI Model (Trust, Respect, Inclusion).
The AI Quality Assurance Engineer is responsible for safeguarding the integrity of AI systems by meticulously overseeing the testing process to identify bugs and inconsistencies. This role serves as the last line of defense, ensuring that only the most reliable AI solutions are deployed.
This role is responsible for designing, developing, and optimizing test cases for AI solutions, ensuring that models and prompts for generative AI are producing the expected outputs prior to full integration into business processes and applications. The incumbent will partner with stakeholders to drive business value to Arbitration Forums and our members through fully tested AI solutions for deployment through a myriad of implementation methods.
DEPARTMENTAL EXPECTATION OF EMPLOYEE
- Adheres to AF Policy and Procedures and the AF IPAAL Values and TRI Model
- Acts as a role model within and outside AF.
- Performs duties as workload necessitates.
- Maintains a positive and respectful attitude.
- Communicates regularly with the departmental leader about department issues.
- Demonstrates flexible and efficient time management and ability to prioritize workload.
- Consistently reports to work on time, prepared to perform duties of the position.
- Meets Department productivity standards
ESSENTIAL DUTIES AND RESPONSIBILITIES
Test Design, Creation, and Optimization:
- Design, develop, and implement comprehensive test plans to ensure AI system functionality aligns with specifications.
- Design automated test cases and monitor results to inform mitigation actions in case of degradation.
- Conduct automated and manual tests to evaluate the AI system’s performance under different scenarios and deployment methods.
- Collaborate with Data Scientist and GenAI Engineers to pinpoint issues and refine the AI system.
- Document test results and feedback into the development cycle for continuous improvement.
- Stay abreast of new testing tools and methodologies to enhance testing efficacy and cost effectiveness.
- Ensuring the AI system adheres to regulatory standards and ethical considerations.
- Collaborate with cross\-functional teams to ensure that test cases are traceable to the original requirements.
- Conduct and automate A/B testing of variants and diversions when needed, including deviations and degradations.
- Collaborate with stakeholders to integrate tested AI solutions into business processes, products, or workflows.
- Script test cases for automation and maintain a reusable library of test cases.
- Partner with the AI Engineers, MLOps Engineer, and other stakeholders to establish and implement observability and monitoring frameworks to adequately and timely identify degradations and potential ethical/bias issues.
- Work with AI engineers to establish and implement explainability frameworks to ensure ability to demonstrate how AI systems work, why they work that way, and how the data is used to produce specific outcomes.
- Collaborate with stakeholders in the simulation of different user behaviors to measure end\-to\-end AI system resilience.
- Provide recommendations for improvement in the areas of AI acceptable use and ethics, collaborating with Legal and Compliance to ensure adherence.
- Define the requirements and processes, as well as own, the AI Quality Assurance environment for production\-mirror testing of data and AI solutions.
AI Governance and Security:
- Ensure the data continuum for testing purposes.
- Ensure that data security protocols are tested in AI solutions in accordance with regulatory requirements and company policies.
Collaboration and Strategy:
- Work closely with IT, product architecture, data engineers, data analysts, data scientists, and business stakeholders to understand needs, data requirements, and test AI solutions.
- Provide technical leadership and mentorship to junior data team members.
- Provide training and support to team members on effective AI testing strategies.
ADDITIONAL DUTIES AND RESPONSIBILITIES
- Lead AI systems and model observability efforts to ensure adherence to company policies and enforce governance standards.
- Other duties as assigned by manager or project needs.
QUALIFICATIONS
- Bachelor’s or Master’s degree in Computer Science, Information Systems, Data Science, Linguistics, or a related field.
- Minimum of 7 years of experience in AI solution testing, software quality assurance, data governance, data science, or a related role.
- Expertise in complex testing frameworks with combined AI and programmatic solutions, including multiple deployment methods.
Technical Skills:* Advanced programming knowledge, including mastery of programming languages such as Python and Java.
- Deep understanding of machine learning and AI principles, from generative models to predictive advanced algorithms.
- Cloud computing and knowledge for deploying and managing AI applications on cloud platforms like Microsoft Azure. Understanding of containerization technologies like Docker and orchestration tools like Kubernetes for scaling AI solutions.
- Data management knowledge, including data pre\-processing, augmentation, and generation of synthetic data, including the cleaning, labeling, and augmenting of data to train and improve AI models.
- Strong understanding of natural language processing concepts and techniques.
- Proficiency in programming languages such as Python, R, and familiarity with AI frameworks (e.g., TensorFlow, Selenium, PyTorch).
- Working knowledge of testing frameworks, from techniques to automate web applications to validation of machine learning models, including Model Context Protocols and SHAP.
- Experience with continuous integration and delivery in the context of DevOps and MLOps.
- Working knowledge of cloud services (i.e., MS Azure, AWS, Google Cloud).
- Experience with AI tools, such as MS Azure ML, Databricks AI, Snowflake CortexAI, Dataiku.
- Strong knowledge of data governance, data security, and compliance practices.
- Familiarity with data visualization and reporting tools (e.g., Webfocus, Power BI).
Soft Skills:* Excellent analytical and problem\-solving abilities.
- Strong communication and interpersonal skills to collaborate with cross\-functional teams.
- Ability to lead projects and mentor junior staff.
- Auto Insurance claims industry experience preferred.
AMERICANS WITH DISABILITY SPECIFICATIONS
PHYSICAL DEMANDS
The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job.
While performing the duties of this job, the employee is occasionally required to stand; walk; sit; use hands to finger, handle, or feel objects, tools, or controls; reach with hands and arms; climb stairs; balance; stoop, kneel, crouch or crawl; talk or hear; taste or smell. The employee must occasionally lift and/or move up to 25 pounds. Specific vision abilities required by the job include close vision, distance vision, color vision, peripheral vision, depth perception, and the ability to adjust focus.
WORK ENVIRONMENT
This is a fully remote position requiring reliable high\-speed internet access and a dedicated workspace.
Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
Salary Context
This $95K-$146K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Arbitration Forums Inc., 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. This role's midpoint ($120K) sits 28% below the category median. Disclosed range: $95K to $146K.
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.
Arbitration Forums Inc. AI Hiring
Arbitration Forums Inc. has 4 open AI roles right now. They're hiring across AI/ML Engineer. Based in Tampa, FL, US. Compensation range: $146K - $233K.
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.
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