Interested in this AI/ML Engineer role at AIG?
Apply Now →Skills & Technologies
About This Role
Be part of something groundbreaking
At AIG, we are making long\-term investments in a brand\-new, innovative Generative AI team, designed to explore new possibilities for how artificial intelligence can be applied in insurance and beyond, and we need your help.
With the support and investment needed to explore new frontiers in generative AI, you’ll be working alongside talented colleagues, innovating and leading projects that will transform how we manage risk and serve our customers.
This team is central to our vision of the future and the core of our business offering. We will incorporate best\-in\-class engineering and product management principles, and your guidance and collaboration will be critical to its success. To rapidly advance and innovate, we need your skills and expertise to build world\-class products. If you’re excited by the opportunity to create meaningful impact on a scale, we’d love to hear from you.
Who we are
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.
At AIG, we’re reshaping how the world manages risk, and we’re inviting you to be a key part of that transformation. As an AI Observability Engineer, you will have the opportunity to make a meaningful impact, leveraging and further developing your skills to guide groundbreaking AI initiatives. If you’re looking for a place to grow your career and where your contributions will shape the future, AIG is where you belong.
How you will create impact
The AI Observability Engineer plays a critical role in interaction with our AI product teams to ensure the AI solutions are aligned with the enterprise Responsible AI principles and standards. Additionally, the AI Observability Analyst supports the design and execution of the overall AI Governance program as well as the communication and awareness initiatives associated with Responsible AI principles and methods through the following activities:
- Model Performance Monitoring: Tracking ML,LLM, Agentic AI key performance indicators (KPIs) like context relevance, accuracy, precision, recall, and F1 scores to ensure model effectiveness
- Data Quality and Bias Detection: Monitoring data inputs for anomalies, bias, and drift, which can impact the fairness and accuracy of AI systems
- Utilize observability tools to produce structured, trustworthy insights from detailed data sets on AI system activities.
- Manage on\-going operations of our AI model management platform and pipelines including evolving our KPIs and model observability capabilities
- Review AI solutions across the business to ensure alignment with AIG’s Organizational Policies, AI Governance Framework and Responsible AI principles
- Provide support to AI product teams, conduct independent reviews, responding to ad\-hoc queries, and participating in workshops
- Collaborate with legal, compliance, security, and other teams to coordinate the review of Responsible AI issues, ensuring a cohesive approach across the organization
- Create executive\-level reports on Responsible AI and provide strategic recommendations to senior leadership
- Create and update RAI training content for technical and non\-technical audiences at all levels of seniority
- Contribute to the development and improvement of RAI assets: methods, technical tutorials, tools
- Monitor and remain up to date on emerging global AI regulatory frameworks, standards and policies by participating in workshops, reading publications, networking with the goal of helping to translate into RAI framework updates
What You’ll Bring
Required Skills/Experience
- Minimum 3 years’ experience and a Bachelor’s degree is required.
- Machine Learning, Large Language Models knowledge of various models, algorithms, and evaluation metrics is crucial for effective monitoring.
- Data pipelines and analysis skills with the ability to handle, clean, and transform data for monitoring and analysis.
- Excited about the problem of scaling human oversight of AI systems and familiar with LLMs development
- Comfort with ambiguity and curiosity as our team is scaling with strong ownership
- You bring experience and passion in Responsible AI or AI ethics, with an understanding of AI technologies and frameworks such as Generative AI
- You have a strong ability to collaborate with cross\-functional teams and drive the implementation of Responsible AI practices, ideally within financial services or other regulated industries
- You have a working knowledge of effective policies and processes around the usage of emerging AI tools and products, bringing best practices from the industry
- You have a proven track record of solving complex problems, developing mitigation strategies, and influencing others on key AI initiatives
- You are passionate about staying ahead of emerging global AI regulations and translating these frameworks into practical, actionable updates
Nice to have
- Prior experience building production LLM, RAG or model inference monitoring pipelines
- Instrumenting applications using OpenTelemetry distributed tracing or structured logs.
- Hands on experience with AI Observability tools such as Fiddler, Arize, LangSmith or similar tools.
Ability to build dashboards and alerts tied to AI model behavior beyond system uptime.
*
\#LI\-PS1
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:
DT \- Data
AIG PC Global Services, Inc.
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 AIG, 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. Mid-level AI roles across all categories have a median of $165,000.
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.
AIG AI Hiring
AIG has 5 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Atlanta, GA, US, New York, NY, US, Jersey City, NJ, US. Compensation range: $280K - $280K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.