Job Description
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.
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Position: Senior Manager, AI Platform Architecture
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Location: Bolingbrook , IL 60440, (Hybrid in Bollingbrook, IL – Tues, Wed, Thurs – every other month)
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Duration: Full Time : Permanent
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Job Description:
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Agentic AI platform design and architecture
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Multi-agent orchestration patterns
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State and memory management approaches
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LLM-as-a-Judge frameworks
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Ideal Candidate:
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Senior Manager, AI Platform Engineering or AI Platform Architect with 12–18 years of experience building enterprise AI/ML platforms. Strong background in Databricks, cloud architecture, MLOps, platform engineering, AI governance, and leading teams of engineers and architects. Experience supporting AI model development and deployment at scale while partnering with data science, product, security, and infrastructure team
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What we're looking for
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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.
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The strongest candidates should be able to discuss:
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Agentic AI platform design and architecture
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Multi-agent orchestration patterns
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State and memory management approaches
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LLM-as-a-Judge frameworks
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MCP (Model Context Protocol) servers and agent integration frameworks
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RAG architectures, context management, and knowledge services
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Semantic layer strategy and tooling
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Human-in-the-loop workflows
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Prompt management and agent lifecycle/versioning
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AI platform governance and operational controls
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Security & Governance Depth
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Candidates should be able to describe:
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AI permissions and security strategies
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Identity and access management approaches
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Multi-agent security frameworks
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Strategies for securing sensitive data in LLM environments
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Model Armor, guardrails, and enterprise AI controls
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Compliance, auditability, and responsible AI practices
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AI Platform Operations & Observability
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We're specifically looking for leaders who have personally driven or architected:
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MLOps / AIOps frameworks
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Logging and monitoring pipelines
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Agent and model observability
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Cost observability and optimization
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Datadog and/or similar observability platforms
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Continuous training and deployment pipelines
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CI/CD processes for AI platforms
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Cloud & Platform Architecture
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The ideal candidate should be able to discuss trade-offs and design decisions across:
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Vertex AI / GCP
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Databricks
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OpenAI ecosystem
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Gemini ecosystem
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Build vs. buy decisions
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Platform selection criteria
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Enterprise-scale AI infrastructure design
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Example of the level of detail we're seeking
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Rather than saying:
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"I led an AI platform team that built agents."
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We'd expect candidates to be able to explain:
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"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."
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Description:
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Senior Manager, AI Platform Architecture
\n
\n
What we're looking for
\n
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.
\n
\n
The strongest candidates should be able to discuss:
\n
Agentic AI platform design and architecture
\n
Multi-agent orchestration patterns
\n
State and memory management approaches
\n
LLM-as-a-Judge frameworks
\n
MCP (Model Context Protocol) servers and agent integration frameworks
\n
RAG architectures, context management, and knowledge services
\n
Semantic layer strategy and tooling
\n
Human-in-the-loop workflows
\n
Prompt management and agent lifecycle/versioning
\n
AI platform governance and operational controls
\n
Security & Governance Depth
\n
Candidates should be able to describe:
\n
AI permissions and security strategies
\n
Identity and access management approaches
\n
Multi-agent security frameworks
\n
Strategies for securing sensitive data in LLM environments
\n
Model Armor, guardrails, and enterprise AI controls
\n
Compliance, auditability, and responsible AI practices
\n
AI Platform Operations & Observability
\n
\n
We're specifically looking for leaders who have personally driven or architected:
\n
MLOps / AIOps frameworks
\n
Logging and monitoring pipelines
\n
Agent and model observability
\n
Cost observability and optimization
\n
Datadog and/or similar observability platforms
\n
Continuous training and deployment pipelines
\n
CI/CD processes for AI platforms
\n
Cloud & Platform Architecture
\n
\n
The ideal candidate should be able to discuss trade-offs and design decisions across:
\n
Vertex AI and GCP
\n
Databricks
\n
OpenAI ecosystem
\n
Gemini ecosystem
\n
Build vs. buy decisions
\n
Platform selection criteria
\n
Enterprise-scale AI infrastructure design
\n
Example of the level of detail we're seeking
\n
Rather than saying:
\n
"I led an AI platform team that built agents."
\n
We'd expect candidates to be able to explain:
\n
"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."
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Going forward, we'd appreciate candidates who have demonstrable hands-on architecture experience in enterprise AI platforms, not primarily people leadership or program oversight. We are specifically looking for leaders who can speak in detail about agentic architectures, MLOps/AIOps, AI governance, platform security, observability, Vertex AI/Databricks ecosystems, and the technical design decisions behind those implementations. The ideal candidate should be comfortable operating at both the leadership level and the architectural implementation level.
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Retail or eCommerce experience
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Generative AI / LLM platform experience
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Agentic AI frameworks and implementations
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Snowflake, Kafka, Spark
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Advanced observability and monitoring platforms
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Multi-cloud experience
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Responsible AI governance programs
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AI cost optimization experience
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Databricks certifications
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Experience leading globally distributed teams
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Nice to Have Skills:
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Retail or eCommerce experience
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Generative AI / LLM platform experience
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Agentic AI frameworks and implementations
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Snowflake, Kafka, Spark
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Advanced observability and monitoring platforms
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Multi-cloud experience
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Responsible AI governance programs
\n
AI cost optimization experience
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Databricks certifications
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Experience leading globally distributed teams
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Technologies resource will be touching:
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Databricks
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GCP
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AWS
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Azure
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AI/ML platforms
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MLOps frameworks
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CI/CD pipelines
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