Head of AI Platform Engineering - Execution Plane

Head Of Ai Platform Engineering – Execution Plane

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:

• 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, 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,…