Senior Manager, AI Platform Architecture

We have a full time opportunity with one of our major clients. They are looking for a Senior Manager, AI Platform Architecture for an important project. Location: Bollingbrook , IL 60440, (Hybrid in Bollingbrook, IL – Tues, Wed, Thurs – every other month) Duration: Full Time : Permanent Job Description: Agentic AI platform design and architecture Multi-agent orchestration patterns State and memory management approaches LLM-as-a-Judge frameworks What we're looking for We need candidates who can go beyond strategy and team leadership and speak in detail about the architecture and implementation of enterprise AI platforms. Now do we need them to be able to go deep on everything below? No, that’s not realistic, but I hope this helps to paint a better picture of what to target in future conversations. The strongest candidates should be able to discuss: Agentic AI platform design and architecture Multi-agent orchestration patterns State and memory management approaches LLM-as-a-Judge frameworks MCP (Model Context Protocol) servers and agent integration frameworks RAG architectures, context management, and knowledge services Semantic layer strategy and tooling Prompt management and agent lifecycle/versioning AI platform governance and operational controls Security & Governance Depth Candidates should be able to describe: AI permissions and security strategies Identity and access management approaches Multi-agent security frameworks Strategies for securing sensitive data in LLM environments Model Armor, guardrails, and enterprise AI controls Compliance, auditability, and responsible AI practices AI Platform Operations & Observability We're specifically looking for leaders who have personally driven or architected: MLOps / AIOps frameworks Logging and monitoring pipelines Agent and model observability Cost observability and optimization Datadog and/or similar observability platforms Continuous training and deployment pipelines CI/CD processes for AI platforms The ideal candidate should be able to discuss trade-offs and design decisions across: Databricks OpenAI ecosystem Build vs. buy decisions Platform selection criteria Example of the level of detail we're seeking Rather than saying: "I led an AI platform team that built agents." We'd expect candidates to be able to explain: "We standardized on Vertex AI with LangGraph for orchestration, implemented a multi-agent architecture with shared memory services, used RAG backed by Databricks vector search, integrated an MCP layer for tool connectivity, implemented evaluation using LLM-as-a-Judge frameworks, and monitored agent performance and cost through Datadog and custom observability dashboards." #J-18808-Ljbffr Calance