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About This Role
As the Head of AI Platform Engineering – Execution Plane, you will lead the development and implementation of our enterprise platform’s execution layer including management of agentic AI workloads, model gateways, agent runtimes, tool/action gateways, MCP servers, orchestration frameworks, or AI execution engines.
You will:
You will collaborate closely with cross\-functional teams, stakeholders, and technology partners to develop and integrate automation solutions that drive business growth, improve customer experiences, and reduce operational costs.
You have:
- Bachelor’s or master’s degree in computer science, engineering, management or a related field.
- Strong people leadership experience managing software engineering teams, including hiring, coaching, performance management, and developing senior technical talent.
- Deep hands\-on technical background designing, building, and operating production\-grade distributed systems, AI/ML platforms, data platforms, or cloud\-native runtime services.
- Experience leading teams responsible for the execution layer of a platform, including runtime services, model inference, retrieval, orchestration, tool invocation, workflow execution, or production AI operations.
- Ability to own the roadmap and delivery for execution plane capabilities such as model gateways, agent runtimes, tool/action gateways, runtime adapters, MCP services, knowledge bases, retrieval pipelines, inference services, and deployment patterns.
- Strong understanding of data access patterns, enterprise APIs, retrieval\-augmented generation, embeddings, vector/search systems, context engineering, and governed access to systems of record.
- Experience building reliable, scalable, low\-latency execution services with clear contracts, strong observability, graceful degradation, retry patterns, rate limits, and operational resilience.
- Ability to partner with control plane, developer experience, data engineering, security, architecture, operations, and domain application teams to ensure execution technologies operate behind governed platform interfaces.
- Proven ability to drive engineering excellence through design reviews, architecture standards, testing, CI/CD, infrastructure automation, incident response, SLOs, runbooks, and production support mechanisms.
- Strong understanding of enterprise security and compliance requirements for runtime execution, including identity propagation, auditability, data handling, least privilege access, secrets management, and environment isolation.
- Strong communication and influence skills, with the ability to simplify complex runtime, data, and AI execution topics for senior stakeholders while aligning teams around reusable platform patterns.
You will:
- Experience with agentic AI systems, LLM platforms, model gateways, agent runtimes, tool/action gateways, MCP servers, orchestration frameworks, or AI execution engines.
- Experience with AWS\-based AI execution services and cloud\-native patterns, including Bedrock, AgentCore, SageMaker, Lambda, Step Functions, API Gateway, EKS, DynamoDB, S3, IAM, CloudWatch, and related DevOps tooling.
- Familiarity with RAG systems, enterprise search, vector databases, embedding models, rerankers, knowledge bases, context engineering, and governed retrieval from enterprise data sources.
- Experience designing multi\-tenant runtime platforms with environment isolation, workload identities, RBAC/ABAC, cross\-account access patterns, quotas, throttling, and secure service\-to\-service communication.
- Background in MLOps, LLMOps, AIOps, model lifecycle management, evaluation pipelines, model serving, inference optimization, or production AI operations.
- Experience with observability for AI workloads, including distributed tracing, token usage, latency, model/tool errors, cost attribution, quality metrics, safety metrics, and operational dashboards.
- Experience with performance, scalability, and cost optimization for high\-volume runtime services, including caching, batching, streaming, load testing, capacity planning, and GPU/CPU optimization where applicable.
- Experience building reusable platform abstractions, SDKs, runtime adapters, reference architectures, or deployment templates that make execution technologies portable across vendors and use cases.
- Experience integrating enterprise tools, APIs, data services, and workflow systems through governed connectors rather than point\-to\-point application wiring.
- Comfort operating in a regulated, matrixed environment where production safety, auditability, resiliency, and vendor optionality are core design principles.
Location:
- New York, New Jersey or Pennsylvania
- Up to 10% travel within US
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
We offer:
- Meaningful and challenging work opportunities to accelerate technology and innovation in a secure and compliant way
- Competitive compensation
- Excellent medical, dental, supplemental health, life and vision coverage for you and your dependents with no wait period
- Life and disability insurance
- A great 401(k) with match
- Tuition assistance, paid parental leave and backup family care
- Dynamic, modern work environments that promote collaboration and creativity
- Flexible time off, dress code, and work location policies to balance your work and life in the ways that suit you best
- Social responsibility in all aspects of our work. We volunteer within our local communities, create educational alliances with colleges, drive a variety of initiatives in sustainability, and advocate for diversity and inclusion in all that we do.
About Guardian:
Every day, Guardian provides Americans the security they deserve through our insurance and wealth management products and services. Since our founding in 1860, our long\-term view has helped our customers prepare for whatever life brings whether starting a family, planning for the future or taking care of employees. Today, we're a Fortune 250 mutual company and a leading provider of life, disability, dental, and other benefits for individuals, at the workplace and through government sponsored programs. The Guardian community of 9,500 employees and our network of over 2,700 financial representatives is committed to serving with expertise when, where and how our clients need us. Our commitments rest on a strong financial foundation, which at year\-end 2018 included $8\.5 billion in capital and $1\.6 billion in operating income. For more information, please visit guardianlife.com or follow us on Facebook, LinkedIn, Twitter and YouTube.
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.
*Guardian® is a registered trademark of the Guardian Life Insurance Company of America.*
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 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 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($201K) sits 8% below the category median. Disclosed range: $152K to $250K.
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.
Guardian Life AI Hiring
Guardian Life has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span New York, NY, US, Holmdel, NJ, US. Compensation range: $195K - $250K.
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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