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About This Role
DEPARTMENT: Information Technology
JOB TITLE: Cloud Artificial Intelligence Security Lead
JOB CODE: CAISL
REPORTS TO: Information Security \& Technical Services Manager
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 Artificial Intelligence Security Lead is a dynamic, empathetic, and action\-oriented individual who plays a crucial role in ensuring that our AI products and solutions uphold the highest standards of security and compliance.
This role is accountable for the definition and implementation of security design patterns for cloud\-based security services, ensuring that the AI cloud security framework is optimized to support the life cycle of AF’s AI\-powered solutions.
The Artificial Intelligence Security Engineer creates execution strategies that focus on embedding security controls into AI models and solution designs and builds practices to allow proactive rather than reactive focus.
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
- Collaborate with the Data Governance Lead and Compliance SMEs to define and implement the operational procedures for data cataloging and lineage harvesting and plotting, with a focus of ensuring that the data utilized in exploration and throughout the model development lifecycle is secured and compliant with AF’s policies.
- Develop and implement policy driven data protection best practices to ensure AI cloud solutions are protected from data loss.
- Collaborate closely with data scientists, GenAI specialists and developers, and MLOps engineers, to identify potential security vulnerabilities, implement best practices, and ensure compliance with regulatory standards including NIST, SOC 2, and others.
- Lead security assessments, coordinate penetration testing, and ensure vulnerability management for AI systems to proactively mitigate risks.
- Support Data Governance by acting as the security expert throughout the designing, developing, and deploying of secure AI and machine learning applications, with a focus on safeguarding personally identifiable information (PII).
- Stay ahead of emerging cybersecurity threats, privacy regulations, and compliance requirements to ensure that our AI solutions continuously meet and exceed market standards.
- Document security protocols, conduct training sessions, and promote security awareness within the team and organization.
- Engage with stakeholders across multiple disciplines to refine security policies and procedures specific to AI and ML products.
- Design, implement, and execute test approaches to GenAI to identify security flaws, particularly those impacting confidentiality, integrity, or availability of information.
- Partner with Quality Assurance department on the creation, implementation, and execution of test plans and strategies for evaluating the compliance of AI systems, including defining test objectives, selecting suitable testing methods, and identifying test scenarios.
- Support the documentation of test methods, results, and suggestions in clear and brief reports to stakeholders.
- Participate in the automation of security test cases and optimize the coverage and performance of automated test scripts.
- Perform security assessments including creating updating and maintaining threat models and security integration of Gen AI platforms.
- Implement/configure security controls on AI technologies.
- Discuss AI/ML concepts proficiently with data science and ML teams to identify and develop solutions for security issues.
- Support the identification and documentation of defects, irregularities or inconsistencies in AI systems working closely with quality assurance, data scientists, GenAI engineers and AI developers to rectify and resolve them.
- Support Enterprise Risk Management in the assessment of AI systems for ethical considerations and potential biases to make sure they follow ethical standards and encourage inclusivity and diversity.
- Design and implement mitigations, detections, and protections to enhance the security and reliability of AI systems.
- Perform model input and output security including prompt injection and security assurance.
- Assist in the evaluation, selection, and secure configuration of AI/ML tools, libraries, and platforms to empower AF’s teams while maintaining a robust security posture.
- Participate in the development and maintenance of a curated portfolio of approved AI tools and services that align with AF’s security standards and business objectives.
- Assist in incident response, threat modeling, and security architecture reviews as needed.
QUALIFICATIONS
Required Qualifications
- Bachelor's degree in computer science, electrical or computer engineering, statistics, econometrics, or related field, or equivalent work experience.
- 10\+ years of hands\-on experience in cybersecurity or information security.
- 4\+ years of experience with Natural Language Processing (NLP) and Large Language Models (LLM) desired.
- 4\+ years of experience working in Microsoft Azure cloud environments (e.g. Azure Cloud Services, Azure Fabric, Azure Data Factory, Purview Data Governance), as well as Azure AI services, as well as data cataloging practices.
- Familiarity with AI testing frameworks and tools such as TensorFlow, PyTorch, or Kerns
- Deep understanding of Machine Learning lifecycles and MLOps.
- Deep understanding of the security challenges and controls for Large Language Models (LLMs), including prompt injections, data poisoning, and model theft.
- Demonstrated proficiency with AI/ML fundamental concepts and technologies including ML, deep learning, NLP, and computer vision.
- Experience assessing AI systems for ethical considerations and potential biases to make sure they follow ethical standards and encourage inclusivity and diversity.
Preferred Qualifications
- Master's degree in computer science, electrical or computer engineering, statistics, econometrics, or related field, or equivalent work experience.
- 12\+ years of hands\-on experience in cybersecurity or information security.
Core Competencies and Skills
- Ability to work independently to learn new technologies, methods, processes, frameworks/platforms, and systems.
- Ability to stay updated on the latest developments, trends, and best practices in artificial intelligence.
- Ability to collaborate with data engineers, data governance leads, and AI teams to ensure the quality, consistency and relevance of data used for training and testing AI models (includes collecting, preprocessing, and validating data).
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 accommodation may be made to enable individuals with disabilities to perform the essential functions.
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Arbitration Forums Inc. AI Hiring
Arbitration Forums Inc. has 2 open AI roles right now. They're hiring across AI/ML Engineer, MLOps Engineer. Based in Tampa, FL, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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