Swanktek
Senior AI Data Architect with Google Cloud Platform
Swanktek, Santa Ana, California, United States, 92725
Senior AI Data Architect
role, you should focus on these 5 high-impact skill clusters. These combine the “must-have” Google Cloud Platform technical stack with the emerging “Agentic” requirements that define this specific job.
Agentic AI & Orchestration Frameworks The JD specifically mentions
Agentic AI
and
autonomous agents . Look for candidates who move beyond basic chatbots and can build systems that “think” and “act.”
Key Keywords:
Vertex AI Agent Builder, LangChain, LangGraph, CrewAI, AutoGen.
What to look for:
Experience building multi-step workflows where an AI agent uses APIs (tools) to complete a task, rather than just generating text.
Vertex AI & MLOps Lifecycle Since this is a Google Cloud Platform-centric role, the candidate must be an expert in the
Vertex AI
suite. They need to demonstrate they can productionize models, not just build them.
Key Keywords:
Vertex AI Pipelines, Model Registry, Feature Store, Model Monitoring, CI/CD for ML.
What to look for:
Candidates who have experience with “Model Drift” detection and automated retraining pipelines (MLOps).
Google Cloud Platform Data Lakehouse Architecture The “Data” half of the title requires a deep understanding of how to store and process the massive datasets that fuel AI.
Key Keywords:
BigQuery (specifically BigQuery ML and BigLake), Dataproc, Dataflow, Medallion Architecture (Bronze/Silver/Gold).
What to look for:
Experience unifying “Data Lakes” (unstructured storage) with “Data Warehouses” (structured SQL) into a single Lakehouse on Google Cloud Platform.
Generative AI & RAG (Retrieval-Augmented Generation) The role requires architecting solutions using LLMs like
Gemini . The candidate must understand how to “ground” these models in company-specific data.
Key Keywords:
Gemini (Pro/Flash), Vector Databases (Vertex AI Search & Conversation), Prompt Engineering, Embeddings.
What to look for:
Evidence of building RAG architectures where an LLM retrieves real-time data from a database to provide accurate, non-hallucinated answers.
Cross-Functional Technical Leadership At the 15 year level, this person is a “Senior Visionary.” They need to bridge the gap between business ROI and technical implementation.
Key Keywords:
Reference Architectures, Stakeholder Management, Solution Blueprints, Cost Optimization (FinOps).
What to look for:
Experience presenting to CXOs, mentoring data engineering teams, and performing “Vendor/Tool Evaluations” for GenAI.
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role, you should focus on these 5 high-impact skill clusters. These combine the “must-have” Google Cloud Platform technical stack with the emerging “Agentic” requirements that define this specific job.
Agentic AI & Orchestration Frameworks The JD specifically mentions
Agentic AI
and
autonomous agents . Look for candidates who move beyond basic chatbots and can build systems that “think” and “act.”
Key Keywords:
Vertex AI Agent Builder, LangChain, LangGraph, CrewAI, AutoGen.
What to look for:
Experience building multi-step workflows where an AI agent uses APIs (tools) to complete a task, rather than just generating text.
Vertex AI & MLOps Lifecycle Since this is a Google Cloud Platform-centric role, the candidate must be an expert in the
Vertex AI
suite. They need to demonstrate they can productionize models, not just build them.
Key Keywords:
Vertex AI Pipelines, Model Registry, Feature Store, Model Monitoring, CI/CD for ML.
What to look for:
Candidates who have experience with “Model Drift” detection and automated retraining pipelines (MLOps).
Google Cloud Platform Data Lakehouse Architecture The “Data” half of the title requires a deep understanding of how to store and process the massive datasets that fuel AI.
Key Keywords:
BigQuery (specifically BigQuery ML and BigLake), Dataproc, Dataflow, Medallion Architecture (Bronze/Silver/Gold).
What to look for:
Experience unifying “Data Lakes” (unstructured storage) with “Data Warehouses” (structured SQL) into a single Lakehouse on Google Cloud Platform.
Generative AI & RAG (Retrieval-Augmented Generation) The role requires architecting solutions using LLMs like
Gemini . The candidate must understand how to “ground” these models in company-specific data.
Key Keywords:
Gemini (Pro/Flash), Vector Databases (Vertex AI Search & Conversation), Prompt Engineering, Embeddings.
What to look for:
Evidence of building RAG architectures where an LLM retrieves real-time data from a database to provide accurate, non-hallucinated answers.
Cross-Functional Technical Leadership At the 15 year level, this person is a “Senior Visionary.” They need to bridge the gap between business ROI and technical implementation.
Key Keywords:
Reference Architectures, Stakeholder Management, Solution Blueprints, Cost Optimization (FinOps).
What to look for:
Experience presenting to CXOs, mentoring data engineering teams, and performing “Vendor/Tool Evaluations” for GenAI.
#J-18808-Ljbffr