What are the responsibilities and job description for the Senior Manager, AI Platform Architecture position at Calance?
Senior Manager, AI Platform Architecture
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
Human-in-the-loop workflows
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
Cloud & Platform Architecture
The ideal candidate should be able to discuss trade-offs and design decisions across:
Vertex AI / Google Cloud Platform
Databricks
OpenAI ecosystem
Gemini ecosystem
Build vs. buy decisions
Platform selection criteria
Enterprise-scale AI infrastructure design
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."
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.
Retail or eCommerce experience
Generative AI / LLM platform experience
Agentic AI frameworks and implementations
Snowflake, Kafka, Spark
Advanced observability and monitoring platforms
Multi-cloud experience
Responsible AI governance programs
AI cost optimization experience
Databricks certifications
Experience leading globally distributed teams
Nice to Have Skills:
Retail or eCommerce experience
Generative AI / LLM platform experience
Agentic AI frameworks and implementations
Snowflake, Kafka, Spark
Advanced observability and monitoring platforms
Multi-cloud experience
Responsible AI governance programs
AI cost optimization experience
Databricks certifications
Experience leading globally distributed teams
Technologies resource will be touching:
Databricks
Google Cloud Platform
AWS
Azure
AI/ML platforms
MLOps frameworks
CI/CD pipelines
Ideal Candidate:
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
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
Human-in-the-loop workflows
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
Cloud & Platform Architecture
The ideal candidate should be able to discuss trade-offs and design decisions across:
Vertex AI / Google Cloud Platform
Databricks
OpenAI ecosystem
Gemini ecosystem
Build vs. buy decisions
Platform selection criteria
Enterprise-scale AI infrastructure design
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."
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.
Retail or eCommerce experience
Generative AI / LLM platform experience
Agentic AI frameworks and implementations
Snowflake, Kafka, Spark
Advanced observability and monitoring platforms
Multi-cloud experience
Responsible AI governance programs
AI cost optimization experience
Databricks certifications
Experience leading globally distributed teams
Nice to Have Skills:
Retail or eCommerce experience
Generative AI / LLM platform experience
Agentic AI frameworks and implementations
Snowflake, Kafka, Spark
Advanced observability and monitoring platforms
Multi-cloud experience
Responsible AI governance programs
AI cost optimization experience
Databricks certifications
Experience leading globally distributed teams
Technologies resource will be touching:
Databricks
Google Cloud Platform
AWS
Azure
AI/ML platforms
MLOps frameworks
CI/CD pipelines
Ideal Candidate:
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
Salary : $160 - $190