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About This Role
### General Information
Location
Cary, North Carolina
Alternative Location(s)
- Posting Location: New York, New York
- Posting Location: Bridgewater, New Jersey
- Posting Location: Tampa, Florida
- Posting Location: Clarks Summit, Pennsylvania
Working Schedule
Full\-Time
Work Arrangement
Hybrid
Travel Required
10%
Relocation Assistance Available
No
Posted Date
14\-Jul\-2026
Job ID
18641
### Description and Requirements
The Team You Will Join
When you join MetLife’s Global Technology team, you’ll be part of a forward\-thinking group dedicated to shaping the future of digital solutions for customers worldwide. You’ll develop, maintain and support technology applications and delivery, leveraging AI, automation, and contemporary ways of working to enhance experiences and drive business outcomes. Your work will simplify complex processes, improve tech resiliency, and ensure high\-performing, seamless solutions that power life’s most important moments. In this dynamic environment, you’ll collaborate with talented peers across teams and functions, expanding your skills in impactful ways. Ready to push boundaries and set new industry standards? Join us and help drive the future of technology forward.
The Opportunity
Enterprise Architecture ensures a holistic strategy and approach to technology solutions by working broadly and globally across the company. We drive MetLife toward a simplified and consistent architecture, enabling quality, agility, and cost\-effectiveness. We support all areas of MetLife in developing and implementing enterprise strategies across Business Units, Global Operations, Application Development, Information Security, Data \& Analytics, and Infrastructure. MetLife is seeking a Lead Architect to enable AI\-powered development platforms for Citizen Developers across the enterprise. This role focuses on making AI tools and platforms easy to use, while ensuring they meet enterprise standards for security, transparency, and responsible use. The architect will design platforms, guardrails, and reusable patterns that allow Associates and Citizen Developers to quickly build solutions without creating hidden risk, technical debt, or compliance issues. The role balances ease of use with strong governance, ensuring AI capabilities can scale safely across the organization. You will work closely with architecture, security, privacy, and platform teams to define how AI and agent\-based capabilities are delivered consistently and responsibly.
Key Responsibilities
- Domain expertise in AI platforms, agent frameworks, low\-code/no\-code ecosystems, and enterprise integration patterns.
- Design and define reference architectures, reusable patterns, and guardrails for AI\-enabled associate and citizen development.
- Establish governance\-by\-design capabilities, including embedded controls for security, compliance, explainability, and transparency.
- Research and hands\-on testing of new technologies and techniques for use with agent platforms.
- Participant and core contributor to the Center of Excellent for citizen agent development.
- Partner with engineering, security, and architecture teams to validate solutions and drive adoption.
- Enable reuse through templates, components, and accelerators that reduce complexity and technical debt.
- Promote adoption through training, guidance, and mentoring across both technical and business teams.
Required Qualifications
- Bachelor’s degree in engineering discipline or equivalent experience.
- 5\+ years of experience in software engineering or architecture in enterprise environments.
- Experience with modern AI concepts, including agentic AI, MCP servers, and/or the creation of AI plugins and skills.
- Knowledge of cloud\-native architectures, APIs, and secure integration patterns.
- Demonstrated expertise in security, operational, and resiliency architecture principles.
- Experience with platforms like Claude AI, Copilot Studio, or similar ecosystems.
- Demonstrated ability to lead technical initiatives requiring engagement and support from partner organizations.
Preferred Qualifications
- Experience with citizen developer platform enablement at scale.
- Familiarity with responsible AI concepts such as explainability and auditability.
- Cloud PaaS technologies, Power BI, Kubernetes, web APIs, API security protocols, etc., with preferred experience in Azure.
- Experience working in large enterprise environments, preferably in financial services.
- Demonstrated influencing and negotiation skills.
- Demonstrated experience with enabling technology adoption in an Enterprise environment.
Location Expectation: This is a hybrid role requiring a minimum of 3 days per week in office.
*The expected salary range for this position is$105,000 \- $161,600. This role may also be eligible for annual short\-term incentive compensation and stock\-based long\-term incentives. All incentives and benefits are subject to the applicable plan terms.*
Benefits We Offer
Our U.S. benefits address holistic well\-being with programs for physical and mental health, financial wellness, and support for families. We offer a comprehensive health plan that includes medical/prescription drug and vision, dental insurance, and no\-cost short\- and long\-term disability. We also provide company\-paid life insurance and legal services, a retirement pension funded entirely by MetLife and 401(k) with employer matching, group discounts on voluntary insurance products including auto and home, pet, critical illness, hospital indemnity, and accident insurance, as well as Employee Assistance Program (EAP) and digital mental health programs, parental leave, paid time off, paid holidays, volunteer time off, tuition assistance and much more!
About MetLife
Recognized on Fortune magazine's list of the "World's Most Admired Companies", Fortune World’s 25 Best Workplaces™, as well as the Fortune 100 Best Companies to Work For®, MetLife, through its subsidiaries and affiliates, is one of the world’s leading financial services companies; providing insurance, annuities, employee benefits and asset management to individual and institutional customers. With operations in more than 40 markets, we hold leading positions in the United States, Latin America, Asia, Europe, and the Middle East.
Our purpose is simple \- to help our colleagues, customers, communities, and the world at large create a more confident future. United by purpose and guided by our core values \- Win Together, Do the Right Thing, Deliver Impact Over Activity, and Think Ahead \- we’re inspired to transform the next century in financial services. At MetLife, it’s \#AllTogetherPossible. Join us!
*MetLife is an Equal Opportunity Employer. All employment decisions are made without regards to race, color, national origin, religion, creed, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity or expression, age, disability, marital or domestic/civil partnership status, genetic information, citizenship status (although applicants and employees must be legally authorized to work in the United States), uniformed service member or veteran status, or any other characteristic protected by applicable federal, state, or local law (“protected characteristics”).* *If you need an accommodation due to a disability, please email us at accommodations@metlife.com. This information will be held in confidence and used only to determine an appropriate accommodation for the application process.*
*MetLife maintains a drug\-free workplace.*
Salary Context
This $105K-$161K range is in the lower quartile 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 MetLife, 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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($133K) sits 39% below the category median. Disclosed range: $105K to $161K.
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.
MetLife AI Hiring
MetLife has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span New York, NY, US, Cary, NC, US. Compensation range: $161K - $318K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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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