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About This Role
Role Purpose
Lead the execution of Guardian’s AI governance program by operationalizing enterprise policies, standards, and guardrails across AI models, copilots, agents, and RAG‑based workflows. This role ensures AI governance is consistently implemented, scalable, and embedded into delivery and platform workflows—enabling responsible AI adoption while managing risk.
The AI Governance Lead partners closely with Data Science, AI Platforms, Data Management, Data Governance, Risk, Legal, and Technology teams to translate enterprise AI governance strategy into practical processes, controls, and day‑to‑day ways of working.
Responsibilities
AI Governance Execution \& Program Leadership
- Lead the execution of Guardian’s AI governance program across analytics, AI, and agentic use cases.
- Implement enterprise AI governance policies, standards, and guardrails in alignment with defined risk appetite and regulatory expectations.
- Ensure governance processes are repeatable, risk‑based, and scalable—not bespoke or ad hoc.
Operating Model \& Workflow Integration
- Own the AI governance operating model, including intake, review, escalation, monitoring, and change management processes.
- Govern AI model lifecycles and AI system behaviors across models, copilots, agents, and RAG workflows, including retrieval strategies, tool use, actions, and human‑in‑the‑loop requirements.
- Embed AI governance controls into platforms and delivery workflows so controls are observable, durable, and auditable.
Standards, Controls \& Risk Management
- Operationalize governance standards for AI development, deployment, monitoring, and retirement.
- Implement risk‑based controls and escalation paths based on use case type, data sensitivity, and impact.
- Partner with Risk, Legal, Compliance, and Security teams to ensure AI governance execution meets internal and external requirements.
Cross‑Functional Partnership
- Serve as the primary AI governance partner to data science, analytics, engineering, and product teams.
- Coordinate with Data Management and Data Governance leaders to ensure AI governance aligns with data controls, quality expectations, and AI‑readiness requirements.
- Support enterprise governance forums by preparing materials, surfacing risks, and enabling timely decision‑making.
Measurement \& Continuous Improvement
- Define and track operational KPIs for AI governance execution, including coverage, exceptions, monitoring effectiveness, and remediation trends.
- Identify gaps, inefficiencies, and emerging risks; recommend adjustments to processes, controls, and tooling.
- Continuously improve governance mechanisms as AI capabilities and use cases evolve.
Leadership \& Influence
- Provide leadership to AI governance practitioners and analysts; set priorities, manage workload, and build governance expertise.
- Influence senior stakeholders across business, technology, and risk through strong execution, transparency, and practical problem‑solving.
Requirements
- 12\+ years of experience in data, analytics, AI, technology risk, or related disciplines.
- Demonstrated experience governing AI systems, including models and agentic workflows, at enterprise scale.
- Strong understanding of AI lifecycle considerations, data usage patterns, and risk management principles.
- Proven ability to develop and translate strategy into executable standards, workflows, and operating models.
- Experience influencing senior stakeholders across business, technology, risk, and legal teams.
- Excellent communication skills and ability to operate through influence rather than direct control.
Salary Range:
$152,290\.00 \- $250,195\.00
The salary range reflected above is a good faith estimate of base pay for the primary location of the position. The salary for this position ultimately will be determined based on the education, experience, knowledge, and abilities of the successful candidate. In addition to salary, this role may also be eligible for annual, sales, or other incentive compensation.
Our Promise
At Guardian, you’ll have the support and flexibility to achieve your professional and personal goals. Through skill\-building, leadership development and philanthropic opportunities, we provide opportunities to build communities and grow your career, surrounded by diverse colleagues with high ethical standards.
Inspire Well\-Being
As part of Guardian’s Purpose – to inspire well\-being – we are committed to offering contemporary, supportive, flexible, and inclusive benefits and resources to our colleagues. Explore our company benefits at www.guardianlife.com/careers/corporate/benefits. *Benefits apply to full\-time eligible employees. Interns are not eligible for most Company benefits.*
Equal Employment Opportunity
Guardian is an equal opportunity employer. All qualified applicants will be considered for employment without regard to age, race, color, creed, religion, sex, affectional or sexual orientation, national origin, ancestry, marital status, disability, military or veteran status, or any other classification protected by applicable law.
Accommodations
Guardian is committed to providing access, equal opportunity and reasonable accommodation for individuals with disabilities in employment, its services, programs, and activities. Guardian also provides reasonable accommodations to qualified job applicants (and employees) to accommodate the individual's known limitations related to pregnancy, childbirth, or related medical conditions, unless doing so would create an undue hardship. If reasonable accommodation is needed to participate in the job application or interview process, to perform essential job functions, and/or to receive other benefits and privileges of employment, please contact [email protected]. Please note: this resource is for accommodation requests only. For all other inquires related to your application and careers at Guardian, refer to the Guardian Careers site.
Visa Sponsorship
Guardian is not currently or in the foreseeable future sponsoring employment visas. In order to be a successful applicant. you must be legally authorized to work in the United States, without the need for employer sponsorship.
Notice Regarding Guardian’s Use of Artificial Intelligence in Recruitment
As part of Guardian’s job application process, Guardian may use artificial intelligence tools (“AI Tools") to automate the sorting and filtering of information provided by applicants as part of its preliminary screening. This preliminary screening may be used to help identify applicant materials and resumes relative to their indication that the applicant meets the requirements for the specific job for which they are applying, as specified in the listing posted on Guardian’s jobs website (Careers at Guardian at https://www.guardianlife.com/careers). At Guardian, we do not use AI Tools to substantially assist or replace human judgment or discretionary decision making in our hiring process. All hiring decisions will be made by Guardian colleagues.
Please be aware that if you apply for a specific position with Guardian, you will have the choice of opting out of Guardian’s use of AI Tools during the job application process. If you would like to request an alternative process that does not utilize AI Tools or would like to request a reasonable accommodation, within ten business days of your position application, you must email your request to [email protected], making sure to provide your name and job requisition identification number. Guardian will retain your applicant materials and resume and all information therefrom in accordance with Guardian’s document retention policy, a copy of which you may request via [email protected].
Additionally, at applicable times, Guardian will make public the most recent bias audit results for such AI tools, which may be found here.
Current Guardian Colleagues: Please apply through the internal Jobs Hub in Workday.
Salary Context
This $152K-$250K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Guardian Life, 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. This role's midpoint ($201K) sits 11% above the category median. Disclosed range: $152K to $250K.
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.
Guardian Life AI Hiring
Guardian Life has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $156K - $250K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% above the national 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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