Senior AI Solution Architect(POS Exprience)
JD below:
Senior AI Solution 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.
1. 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."
ā¢ Key Keywords: Vertex AI Agent Builder, LangChain, LangGraph, CrewAI, AutoGen.
ā¢ 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.
2. 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.
ā¢ Key Keywords: Vertex AI Pipelines, Model Registry, Feature Store, Model Monitoring, CI/CD for ML.
ā¢ What to look for: Candidates who have experience with "Model Drift" detection and automated retraining pipelines (MLOps).
3. 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.
ā¢ Key Keywords: BigQuery (specifically BigQuery ML and BigLake), Dataproc, Dataflow, Medallion Architecture (Bronze/Silver/Gold).
ā¢ What to look for: Experience unifying "Data Lakes" (unstructured storage) with "Data Warehouses" (structured SQL) into a single Lakehouse on Google Cloud Platform.
4. 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.
ā¢ Key Keywords: Gemini (Pro/Flash), Vector Databases (Vertex AI Search & Conversation), Prompt Engineering, Embeddings.
ā¢ 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.
5. Cross-Functional Technical Leadership
At the 10-15+ year level, this person is a "Senior Visionary." They need to bridge the gap between business ROI and technical implementation.
ā¢ Key Keywords: Reference Architectures, Stakeholder Management, Solution Blueprints, Cost Optimization (FinOps).
ā¢ What to look for: Experience presenting to CXOs, mentoring data engineering teams, and performing "Vendor/Tool Evaluations" for GenAI.
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Senior AI Solution 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.
1. 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."
ā¢ Key Keywords: Vertex AI Agent Builder, LangChain, LangGraph, CrewAI, AutoGen.
ā¢ 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.
2. 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.
ā¢ Key Keywords: Vertex AI Pipelines, Model Registry, Feature Store, Model Monitoring, CI/CD for ML.
ā¢ What to look for: Candidates who have experience with "Model Drift" detection and automated retraining pipelines (MLOps).
3. 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.
ā¢ Key Keywords: BigQuery (specifically BigQuery ML and BigLake), Dataproc, Dataflow, Medallion Architecture (Bronze/Silver/Gold).
ā¢ What to look for: Experience unifying "Data Lakes" (unstructured storage) with "Data Warehouses" (structured SQL) into a single Lakehouse on Google Cloud Platform.
4. 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.
ā¢ Key Keywords: Gemini (Pro/Flash), Vector Databases (Vertex AI Search & Conversation), Prompt Engineering, Embeddings.
ā¢ 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.
5. Cross-Functional Technical Leadership
At the 10-15+ year level, this person is a "Senior Visionary." They need to bridge the gap between business ROI and technical implementation.
ā¢ Key Keywords: Reference Architectures, Stakeholder Management, Solution Blueprints, Cost Optimization (FinOps).
ā¢ What to look for: Experience presenting to CXOs, mentoring data engineering teams, and performing "Vendor/Tool Evaluations" for GenAI.
Apply tot his job
Apply To this Job