Head of AI Platform Engineering - Execution Plane

Job Overview

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.

• 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.

• 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.
• 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 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 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.

• Deep hands‑on technical background designing, building, and operating production‑grade distributed systems, AI/ML platforms, data platforms, or cloud‑native runtime services.
• Deep 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.
• 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, Agent Core, Sage Maker, Lambda, Step Functions, API Gateway, EKS, DynamoDB, S3, IAM, Cloud Watch, and related Dev Ops 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.

• New York, New Jersey or…