Head of AI Platform Engineering - Execution Plane at Guardian in New York, NY

Job description

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 testin