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About This Role
DEPARTMENT: Data Insights and Innovation
JOB TITLE: GenAI and Agentic AI Engineer
JOB CODE: GAIAAE
REPORTS TO: Manager, AI Solutions
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 GenAI and Agentic AI Engineer is responsible for designing, developing, optimizing, and testing generative AI and agentic AI solutions to enable the production of accurate, relevant, and high\-quality outputs across various business applications. The ideal candidate will have a strong background in AI, natural language processing, and RPA. and deep expertise in Large Language Models, RAG, prompt engineering, and Robotic Process Automation.
The GenAI and Agentic AI Engineer will partner with stakeholders to drive business value to Arbitration Forums and our members through GenAI and Agentic AI solutions. This role excels at the implementation of the agentic AI development process, employing AI techniques to guide and enhance solutions, developing effective AI interactions and automations through proficient programming and testing.
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
- Prompt Design and Optimization:
+ Design, develop, and refine prompts for LLMs to ensure high\-quality outputs for specific use cases.
+ Employ techniques to guide and enhance model responses, ensuring that the AI interactions are effective and efficient.
+ Develop effective AI interactions through proficient programming and utilization of playgrounds, including the implementation and manipulation of complex algorithms fundamental to developing generative AI models.
+ Articulate, design, develop, and implement Agentic AI solutions, following an established development process that is inclusive of the agentic solution lifecycle.
+ Collaborate with cross\-functional teams to ensure that the solutions are aligned with the business requirements and objectives.
+ Conduct A/B testing of prompt variants and automation solutions, analyzing model behaviors and agentic paths, including deviations and degradations.
+ Stay up to date with advancements in LLM and RAG capabilities, prompt engineering, and Agentic AI best practices.
+ Collaborate with product managers, data scientists, ML engineers, and business stakeholders to integrate GenAI and AI solutions into business processes, products, or workflows.
+ Create documentation and reusable libraries for internal and external use.
+ Partner with the MLOps Engineer and other stakeholders to establish and implement observability and monitoring frameworks to adequately and timely identify degradations and potential ethical/bias issues.
+ Establish, embed, and implement explainability frameworks in AI solutions, through Model Context Protocols and SHAP.
+ Provide recommendations for improvement in the areas of AI acceptable use and ethics, collaborating with Legal and Compliance to ensure adherence.
- AI Governance and Security:
+ Ensure data quality and integrity as they apply to GenAI, Agentic AI, and NLP.
+ Ensure that data security protocols are followed in the definition and implementation of GenAI and Agentic AI solutions in accordance with regulatory requirements and company policies.
+ Develop safety filters and guide the ethical use of prompts and automation paths to prevent biased or harmful responses.
- Collaboration and Strategy:
+ Work closely with IT, product architecture, data engineers, data analysts, data scientists, and business stakeholders to understand needs, data requirements, and implement solutions.
+ Provide technical leadership and mentorship to junior data team members.
+ Provide training and support to team members on effective prompt engineering strategies.
+ Stay updated on emerging data technologies and best practices, making recommendations for continuous improvement.
ADDITIONAL DUTIES AND RESPONSIBILITIES
- Support model and solution 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 NLP, GenAI, LLMs, RAG, data governance, data science, or a related role.
- Deep understanding of the components and development process of an Agentic AI solution.
Technical Skills:
- Advanced programming knowledge, including mastery of programming languages such as Python, and especially AI\-centric libraries like TensorFlow, PyTorch, and Keras.
- Working knowledge of Azure AI Foundry and ML Studio.
- Working knowledge of low code solutions, as well as tool integration and multi\-agent collaboration.
- Experience mapping and defining business processes and/or workflows for automation purposes.
- Cloud computing and knowledge for deploying and managing AI applications on cloud platforms like AWS, Google Cloud, or Microsoft Azure. Deep understanding of containerization technologies like Docker and orchestration tools like Kubernetes for scaling AI solutions.
- Experience combining design patterns like Tool Use, Reflection, and Planning for robust and solid automation.
- Expertise in generative models such as generative adversarial networks (GANs) and variational autoencoders (VAEs). Ability to design, train, and optimize these models to generate high\-quality, creative content.
- Experience in Natural language processing (NLP) for text generation projects. Working knowledge of techniques for text parsing, sentiment analysis, and the use of transformers like GPT (generative pre\-trained transformer) models.
- 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.
- Proficiency prompt engineering for generative AI models (GPT\-4, DALL\-E, etc.) and experience creating and integrating RAG capabilities.
- Strong understanding of natural language processing concepts and techniques.
- Proficiency in programming languages such as Python and familiarity with AI frameworks (e.g., TensorFlow, PyTorch).
- Understanding of Agentic AI frameworks and tools, like CrewAI and Azure AI Foundry.
- 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).
- Proficiency in programming languages such as Python, R, or SQL.
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
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 $131K-$199K 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. Disclosed range: $131K to $199K.
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.
Frequently Asked Questions
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