Head of AI Platform Engineering - Execution Plane

Guardian Life Insurance•New York, NY

4d•$152,290 - $250,195

About The Position

The Head of AI Platform Engineering – Execution Plane will lead development and implementation of the execution layer of Guardian’s enterprise platform. This includes capabilities for managing agentic AI workloads, model gateways, agent runtimes, tool and action gateways, MCP servers, orchestration frameworks, and AI execution engines. The role collaborates with cross-functional teams, stakeholders, and technology partners to develop and integrate automation solutions that support business growth, improve customer experiences, and reduce operational costs. The role owns the roadmap and delivery for execution-plane capabilities and partners with control plane, developer experience, data engineering, security, architecture, operations, and domain application teams. It drives reliable, scalable, low-latency, secure, and observable production services, with strong engineering standards and governed platform interfaces. The position is based in New York, New Jersey, or Pennsylvania and requires up to 10% travel within the United States.

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 a platform execution layer, including runtime services, model inference, retrieval, orchestration, tool invocation, workflow execution, or production AI operations.
Ability to own roadmaps 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.
Ability to build reliable, scalable, low-latency execution services with clear contracts, strong observability, graceful degradation, retry patterns, rate limits, and operational resilience.
Ability to partner with cross-functional platform and application teams to deliver governed execution technologies.
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, including the ability to explain complex technical topics to senior stakeholders and align teams around reusable platform patterns.
Legally authorized to work in the United States without requiring employment-based visa sponsorship now or in the future.

Nice To Haves

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.

Responsibilities

Lead the development and implementation of the enterprise platform’s execution layer.
Collaborate 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, including 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 through governed platform interfaces.
Drive engineering excellence through design reviews, architecture standards, testing, CI/CD, infrastructure automation, incident response, SLOs, runbooks, and production support mechanisms.
Lead and develop software engineering teams, including hiring, coaching, performance management, and development of senior technical talent.
Build reliable, scalable, low-latency execution services with clear contracts, observability, graceful degradation, retry patterns, rate limits, and operational resilience.
Support enterprise security and compliance for runtime execution, including identity propagation, auditability, data handling, least-privilege access, secrets management, and environment isolation.
Communicate complex runtime, data, and AI execution topics to senior stakeholders and align teams around reusable platform patterns.

Benefits

Competitive compensation and possible eligibility for annual, sales, or other incentive compensation.
Medical, dental, supplemental health, life, and vision coverage for employees and dependents with no waiting period.
Life and disability insurance.
401(k) plan with employer match.
Tuition assistance.
Paid parental leave.
Backup family care.
Dynamic, modern work environments that promote collaboration and creativity.
Flexible time off, dress code, and work location policies.
Skill-building, leadership development, and philanthropic opportunities.
Professional and personal development opportunities in a diverse workplace with high ethical standards. Benefits apply to full-time eligible employees; interns are not eligible for most company benefits.