Impress employers and recruiters.
Choose from hundreds of resume examples.

Target Resume Now

Tailor your resume to this Head of AI Platform Engineering - Execution Plane role.

Rezi rewrites your resume against Guardian Life Insurance's job description. Free.

Resume score gauge reading 58 out of 100

Don't guess if your resume is good enough.

See how it scores against the Head of AI Platform Engineering - Execution Plane posting at Guardian Life Insurance — free, in seconds.

About the Role

Lead the development and implementation of the 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. Collaborate with cross-functional teams to develop and integrate automation solutions that drive business growth, improve customer experiences, and reduce operational costs.

Responsibilities

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.
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.
Partner with control plane, developer experience, data engineering, security, architecture, operations, and domain application teams to ensure execution technologies operate behind governed platform interfaces.
Drive engineering excellence through design reviews, architecture standards, testing, CI/CD, infrastructure automation, incident response, SLOs, runbooks, and production support mechanisms.
Integrate enterprise tools, APIs, data services, and workflow systems through governed connectors rather than point-to-point application wiring.

Requirements

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.
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.
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.
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.
Comfort operating in a regulated, matrixed environment where production safety, auditability, resiliency, and vendor optionality are core design principles.

Skills

Distributed systems
AI/ML platforms
Data platforms
Cloud-native runtime services
Execution layer platforms
Runtime services
Model inference
Orchestration
Tool invocation
Workflow execution
Production AI operations
Data access patterns
Enterprise APIs
Retrieval-augmented generation
Embeddings
Vector/search systems
Context engineering
Governed access to systems of record
Reliable services
Scalable services
Low-latency services
Observability
Graceful degradation
Retry patterns
Rate limits
Operational resilience
Enterprise security
Compliance requirements
Identity propagation
Auditability
Data handling
Least privilege access
Secrets management
Environment isolation
Communication
Influence
Agentic AI systems
LLM platforms
Model gateways
Agent runtimes
Tool/action gateways
MCP servers
Orchestration frameworks
AI execution engines
AWS
Bedrock
AgentCore
SageMaker
Lambda
Step Functions
API Gateway
EKS
DynamoDB
S3
IAM
CloudWatch
DevOps tooling
RAG systems
Enterprise search
Vector databases
Embedding models
Rerankers
Knowledge bases
Governed retrieval
Multi-tenant platforms
Workload identities
RBAC/ABAC
Cross-account access patterns
Quotas
Throttling
Service-to-service communication
MLOps
LLMOps
AIOps
Model lifecycle management
Evaluation pipelines
Model serving
Inference optimization
Distributed tracing
Token usage
Latency monitoring
Model/tool error tracking
Cost attribution
Quality metrics
Safety metrics
Operational dashboards
Performance optimization
Scalability optimization
Cost optimization
Caching
Batching
Streaming
Load testing
Capacity planning
GPU optimization
CPU optimization
Platform abstractions
SDKs
Runtime adapters
Reference architectures
Deployment templates
Vendor portability
Use case portability
Enterprise tools integration
API integration
Data services integration
Workflow systems integration
Governed connectors
Production safety
Resiliency
Vendor optionality

Location

New York
New Jersey
Pennsylvania

Work Type

Full-time
Hybrid

Experience Level

Senior
Leadership

Education Level

Bachelor's degree in computer science, engineering, management or a related field
Master's degree in computer science, engineering, management or a related field

Salary/Compensations

$152,290.00 - $250,195.00

Benefits

Support and flexibility to achieve professional and personal goals
Skill-building opportunities
Leadership development opportunities
Philanthropic opportunities
Opportunities to build communities
Career growth opportunities
Diverse colleagues
High ethical standards
Meaningful and challenging work opportunities
Accelerate technology and innovation in a secure and compliant way
Competitive compensation
Excellent medical coverage for you and your dependents with no wait period
Excellent dental coverage for you and your dependents with no wait period
Excellent supplemental health coverage for you and your dependents with no wait period
Excellent life coverage for you and your dependents with no wait period
Excellent vision coverage for you and your dependents with no wait period
Life insurance
Disability insurance
401(k) with match
Tuition assistance
Paid parental leave
Backup family care
Dynamic, modern work environments that promote collaboration and creativity
Flexible time off
Flexible dress code
Flexible work location policies
Social responsibility initiatives
Community volunteering
Educational alliances with colleges
Sustainability initiatives
Diversity and inclusion advocacy

About the Company

Guardian provides Americans security through insurance and wealth management products and services.
Founded in 1860, Guardian has a long-term view to help customers prepare for life's events.
A Fortune 250 mutual company and a leading provider of life, disability, dental, and other benefits.
Guardian community of 9,500 employees and network of over 2,700 financial representatives are committed to serving clients.
Strong financial foundation with $8.5 billion in capital and $1.6 billion in operating income at year-end 2018.

Equal 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 committed to providing access, equal opportunity and reasonable accommodation for individuals with disabilities in employment, its services, programs, and activities.
Guardian 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.
Guardian Life is not currently or in the foreseeable future sponsoring employment-based visas.
Applicants must be legally authorized to work in the United States, without the need for employer sponsorship/support now or at any time in the future.