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About This Role
DEPARTMENT: Data Insights and Innovation
JOB TITLE: Manager, AI SolutionsJOB CODE: MAISREPORTS TO: Chief Innovation OfficerFLSA STATUS: ExemptEMPLOYMENT TYPE: Full\-TimeJOB PURPOSE:
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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 Manager, AI Solutions is responsible for understanding the opportunities brought by emerging technologies and Artificial Intelligence to the end\-to\-end insurance Claims process. This role is critical in the evolution of AF’s current product line functionality via identification of AI\-powered capabilities.
As part of an AI\-powered solution designing and implementation organization, this role will partner with AF’s stakeholders to identify opportunities to embed intelligent decisioning in the claims management ecosystem, utilizing data science, machine learning, robotic process automation, and other AI practices to support our members in reducing the cost of operations, increasing processes quality, and providing meaningful and actionable data\-based insights.
The Manager, AI Solutions articulates strategies for the design, development, optimization and testing of AI models, ensuring the production of accurate, relevant, and compliant outs across AI initiatives. This role is accountable for the implementation of configurable and interoperable AI solutions that optimize the value delivered to our members, and works with AI product architects, advanced analytics, data governance, and other stakeholders to enable AF’s journey to innovation.
This role encompasses data science, Generative AI, Speech and Text analytics, Robotic Process Automation, and other related areas of the AI universe and collaborates with AF’s functions to execute strategic plans that bring innovative solutions and excellent value to our members’ organizations.DEPARTMENTAL EXPECTATION OF EMPLOYEE
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- 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* Partner with stakeholders to identify high value use cases to provide data insights, automation, and forecasting capabilities, with a critical eye for the ones that will result in the highest business value.
- Enable the incorporation of configurable and interoperable AI products from design to deployment, ensuring the alignment of advanced analytics, AI product architecture, IT and product implementation and adoption functions.
- Define best practices in the area of LLMs, prompt engineering, forecasting, and RPA solutions.
- Set the practice for improving model effectiveness and performance through the model lifecycle, including observability and monitoring processes to identify model performance and potential degradation. Establish action plans on mitigation of potential model degradation.
- Collaborate with business architects and process owners to enable the identification of areas and process steps in where RPA brings value.
- Enable the creation of safety filters and ethical use of models to prevent bias, and to promote fairness, following our AI Acceptable Use and Ethics policies.
- Work with the data governance lead to establish and enforce data governance standards, guidelines, processes, and best practices to ensure data quality and integrity as they apply to AI.
- Establish and implement explainability frameworks, like Model Context Protocols and SHAP.
- Ensure that data security protocols are followed in the definition and implementation of AI solutions in accordance with regulatory requirements and company policies.
- Collaborate with stakeholders to generate strategies for embedding AI solutions into business processes and applications.
- Manage the capacity of the team by prioritizing the highest value initiatives, as well as the ones that enable the building of internal capabilities.
- Deliver AI\-powered solutions that contain a high level of interoperability, configurability, and modularization.
- Engage with partners to understand what marketplace deployment models mean from an AI rollout perspective.
- Collaborate with stakeholders in defining implementation options for our members, including reviewing solution paths and roadmaps.
- Enable the identification and prioritization of AI\-powered use cases to ensure integration and deployment requirements are considered.
- Enable effective AI text and speech services through utilization of ASR engines using standard and fit\-for\-purpose frameworks.
- Partner with IT QA and AI QA Engineers to define a testing practice that is efficient, cost\-effective, and automated; implement the necessary tools to enable test efficiency.
- Advocate and promote AI literacy internally and externally.
QUALIFICATIONS* More than 10 years of experience in AI technologies and AI business operations.
- 10 years of experience in the operationalization of AI models and integration with business processes and applications.
- Domain and industry knowledge, with understanding of how Insurance companies and Claims Management divisions work.
- Experience with technology\-driven transformations and end\-to\-end AI solutions lifecycle.
- Deep understanding of diverse deployment options for AI\-based products, from marketplace, to API and programmatic approaches.
- Proficiency in the definition and implementation of prioritization frameworks.
- Expertise in cloud application solutions, API design and management, and marketplace business and deployment models.
- Experience implementing MLOps processes and frameworks.
- Functional knowledge of composable products with a high level of configurability.
- Working knowledge and proficiency in the development of implementation and adoption plans for composable offerings, including documentation of configuration requirements.
- Proficiency in the operations of a not\-for\-profit organization.
- Knowledge of advanced data analytics platforms and visualization tools to create reports and dashboards in a self\-service approach.
- Technical Skills
- Experience with data visualization tools such as Webfocus, Power BI or Tableau.
- Experience defining and managing a data technology stack, enabling the definition and implementation of controls and best practices.
- Understanding of cloud\-based data engineering processes, as well as exposure to data platforms such as Databricks or Snowflake.
- Advanced SQL skills including writing and tuning complex queries, Oracle database experience preferred.
- Experience utilizing data transformation and load technologies such as Talend, DBT, Matillion, etc.
- Experience interacting with Data Science, ML tools such as Dataiku, Azure ML, Snowflake Cortex AI, Databricks AI, etc.
- Advanced understanding of data cataloging and lineage plotting processes and tools.
Technical Skills* Proficiency in utilization of generative AI models (GPT\-4, DALL\-E, etc.).
- Working knowledge of Azure AI Foundry and ML Studio.
- Strong working knowledge of programming languages such as Python, R, or SQL and familiarity with AI frameworks (e.g., TensorFlow, PyTorch).
- Experience with AI tools, such as MS Azure ML, 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).
- Proficiency in programming languages such as Python, R, or SQL.
- Working knowledge of cloud services (MS Azure, AWS) and data platforms (Snowflake, Databricks).
- Exposure to techniques for text parsing, sentiment analysis, and the use of transformers such as (BERT\-base. ELMo, ULM\-FIT.
- Strong knowledge of data governance, data security, and compliance practices.
- Familiarity with data visualization and reporting tools (e.g., Webfocus, Power BI).
- Strong working knowledge of automation tools such as MS Power Automate and Appian.
Leadership* Establish goals for the AI Solutions team and its members, and clearly articulate performance objectives.
- Communicate clearly and directly.
- Delegates effectively, mentoring, and coaching team members to enable their success.
- Live AF’s values and behaviors, holding team members accountable for conducting themselves within the behaviors of the organization.
- Establish engagement models to effectively interact and drive accountability for team members across AF.
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 $153K-$233K range is above the 75th percentile 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 ($193K) sits 16% above the category median. Disclosed range: $153K to $233K.
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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