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About This Role
About IAG
AIG is a leading global insurance organization providing a wide range of property casualty insurance and other financial services. We provide world\-class products and expertise to businesses and individuals in approximately 190 countries and jurisdictions.
Internal Audit Group
AIG’s Internal Audit Group (IAG) provides independent and objective assurance, advice, and insight guided by a philosophy of adding value to improve the operations of AIG. IAG assists AIG in accomplishing its objectives by bringing a systematic and disciplined approach to evaluate and improve the effectiveness of AIG’s control environment including risk management, operational, financial, internal control, and governance processes.
About the role
At AIG, we’re reshaping how the world manages risk, and we’re inviting you to be a key part of that transformation. As a Director Internal Audit\- Data and AI, will have the opportunity to make a meaningful impact, providing independent assurance over AIG’s Data Office, Artificial Intelligence and global infrastructure. You will be providing coverage for Data Office, AI and infrastructure platforms that power our digital ecosystem. This role will report directly to the Chief Technology Auditor.
You will oversee a team of auditors focused on understanding and assessing risks and controls related to data governance, artificial intelligence, and emerging technologies across AIG. This includes managing relationships with the Chief Data and Digital Officer, ensuring audits are completed in compliance with audit methodology and Institute of Internal Auditors’ standards, and developing audit team skillsets in emerging technology domains.
Your impact will include:
- Work with Chief Technology auditor to define audit strategy to address risks associated with data governance, AI risk management, and emerging technology and infrastructure activities. This will include ensuring compliance with global regulations.
- Lead and Oversee the execution of the audit strategy coverage: Execute a global assurance plan and audit methodology for data governance, AI risk management, and emerging technology and infrastructure activities, including continuous monitoring of regulatory developments and best practices.
- Manage and Develop Teams: Build, coach, and maintain a high\-performing global team with advanced data, AI/ML, and technology assurance skillsets; oversee talent management of offshore teams, staff evaluation, and hiring processes.
- Engage Stakeholders and Influence Change: Proactively manage relationships with senior management, and other stakeholders; provide insights to drive sustainable improvements in processes and controls.
- Deliver Audit Outcomes: Draft, review, and communicate audit findings and reports to executive management; participate in steering committees and working groups to promote balanced discussions and continuous improvement.
- Driving the adoption of data analytics, AI, and automation in audit testing and risk assessment to enhance assurance efficiency and insight.
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What we value
- Excellent analytical, written communication, interpersonal, organizational, and presentation skills.
- Strong client relationship and employee management skills.
- Ability to think strategically and multi\-task in a fast\-paced environment.
- Role model desired behaviors in cross\-cultural awareness and establish and maintain a collaborative and inclusive work environment.
- Ability to cooperate with others and foster an environment that supports effective teamwork.
- Highly collaborative with strong leadership skills that gain trust and credibility with colleagues and stakeholders.
- Strategic mindset and ability to clearly articulate vision; ability to drive adoption and measure success of initiatives.
- Experience developing and maintaining relationships with multiple stakeholders, including senior management, and audit staff.
- Ability to manage complexity, effectively prioritize multiple tasks, and work independently in non\-routine situations and in a changing environment.
- Strong communication, interpersonal, and leadership abilities across all levels, coupled with effective problem solving, conceptual thinking, quantitative, and analytical skills.
- A very high degree of professionalism, professional curiosity and skepticism, leadership, proficient organizational, analytical, and project management skills.
- Experience in planning and leading strategic initiatives, including employee engagement. Desire and willingness to drive to the root cause of issues.
Education \& Preferred Qualifications
- 10\+ years of experience with assurance, ideally in financial services, data governance and risk management, and technology innovation as well as 2\+ years of experience auditing AI
- Relevant bachelor’s degree in data science, Computer Science, Engineering, Technology, or other relevant fields. Postgraduate qualifications are desired but not mandatory.
- One industry\-recognized certification (e.g., CPA, CIA, CISA, CISSP, CISM, CDMP, AAIA, AAISM or equivalent) required.
- Deep experience with assurance in a number of the following: Data governance, data privacy, and data protection controls; AI/ML risk management, including AI model development, validation, deployment, and monitoring; Algorithmic bias, fairness, explainability, and ethical considerations in AI systems; Data quality, lineage, and integrity across complex technology environments; Regulatory compliance related to data and AI (e.g., GDPR, CCPA, EU AI Act)
- Experience auditing new AI infrastructure platforms e.g. AWS Bedrock, Claude and Palantir
- Experience auditing infrastructure including cloud, network, compute, storage, databases
- Audit experience in public accounting or internal audit, focusing on regulated industries is preferred.
- Demonstrated experience in leading and developing diverse teams and overseeing multiple large\-scale projects.
- Proven ability to learn innovative products and technologies and execute related assurance.
The base salary range for this position is $180,000\-$210,000 and the position is eligible for a bonus in accordance with the terms of the applicable incentive plan. Your actual compensation will be dependent on your skills, experience, and qualifications. In addition, we’re proud to offer a range of competitive benefits, a summary of which can be viewed here: US Benefits Overview.
At AIG, we value in\-person collaboration as a vital part of our culture, which is why we ask our team members to be primarily in the office. This approach helps us work together effectively and create a supportive, connected environment for our team and clients alike.
Enjoy benefits that take care of what matters
At AIG, our people are our greatest asset. We know how important it is to protect and invest in what’s most important to you. That is why we created our Total Rewards Program, a comprehensive benefits package that extends beyond time spent at work to offer benefits focused on your health, wellbeing and financial security—as well as your professional development—to bring peace of mind to you and your family.
Reimagining insurance to make a bigger difference to the world
American International Group, Inc. (AIG) is a global leader in commercial and personal insurance solutions; we are one of the world’s most far\-reaching property casualty networks. It is an exciting time to join us — across our operations, we are thinking in new and innovative ways to deliver ever\-better solutions to our customers. At AIG, you can go further to support individuals, businesses, and communities, helping them to manage risk, respond to times of uncertainty and discover new potential. We invest in our largest asset, our people, through continuous learning and development, in a culture that celebrates everyone for who they are and what they want to become.
Welcome to a culture of inclusion
We’re committed to creating a culture that truly respects and celebrates each other’s talents, backgrounds, cultures, opinions and goals. We foster a culture of inclusion and belonging through learning, cultural awareness activities and Employee Resource Groups (ERGs). With global chapters, ERGs are a cornerstone for our culture of inclusion. The talent of our people is one of AIG’s greatest assets, and we are honored that our drive for positive change has been recognized by numerous recent awards and accreditations.
*AIG provides equal opportunity to all qualified individuals regardless of race, color, religion, age, gender, gender expression, national origin, veteran status, disability or any other legally protected categories.*
AIG is committed to working with and providing reasonable accommodations to job applicants and employees with disabilities. If you believe you need a reasonable accommodation, please send an email to [email protected].
Functional Area:
IA \- Internal Audit
AIG Employee Services, Inc.
Salary Context
This $180K-$210K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At AIG Employee Services, 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($195K) sits 11% below the category median. Disclosed range: $180K to $210K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
AIG Employee Services, Inc. AI Hiring
AIG Employee Services, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $210K - $210K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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