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<strong>JD Below<br><br></strong><strong>Senior AI Solution Architect</strong>role, you should focus on these 5 high-impact skill clusters. These combine the 'must-have' GCP technical stack with the emerging 'Agentic' requirements that define this specific job.<br><br><ul><li> Agentic AI & Orchestration Frameworks<br><br></li></ul>The JD specifically mentions<strong>Agentic AI</strong>and<strong>autonomous agents</strong>. Look for candidates who move beyond basic chatbots and can build systems that 'think' and 'act.'<br><br><ul><li>Key Keywords:Vertex AI Agent Builder, LangChain, LangGraph, CrewAI, AutoGen.</li><li>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.</li><li> Vertex AI & MLOps Lifecycle<br><br></li></ul>Since this is a GCP-centric role, the candidate must be an expert in the<strong>Vertex AI</strong>suite. They need to demonstrate they can productionize models, not just build them.<br><br><ul><li>Key Keywords:Vertex AI Pipelines, Model Registry, Feature Store, Model Monitoring, CI/CD for ML.</li><li>What to look for:Candidates who have experience with 'Model Drift' detection and automated retraining pipelines (MLOps).</li><li> GCP Data Lakehouse Architecture<br><br></li></ul>The 'Data' half of the title requires a deep understanding of how to store and process the massive datasets that fuel AI.<br><br><ul><li>Key Keywords:BigQuery (specifically BigQuery ML and BigLake), Dataproc, Dataflow, Medallion Architecture (Bronze/Silver/Gold).</li><li>What to look for:Experience unifying 'Data Lakes' (unstructured storage) with 'Data Warehouses' (structured SQL) into a single Lakehouse on GCP.</li><li> Generative AI & RAG (Retrieval-Augmented Generation)<br><br></li></ul>The role requires architecting solutions using LLMs like<strong>Gemini</strong>. The candidate must understand how to 'ground' these models in company-specific data.<br><br><ul><li>Key Keywords:Gemini (Pro/Flash), Vector Databases (Vertex AI Search & Conversation), Prompt Engineering, Embeddings.</li><li>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.</li><li> Cross-Functional Technical Leadership<br><br></li></ul>At the 1015+ year level, this person is a 'Senior Visionary.' They need to bridge the gap between business ROI and technical implementation.<br><br><ul><li>Key Keywords:Reference Architectures, Stakeholder Management, Solution Blueprints, Cost Optimization (FinOps).</li><li>What to look for:Experience presenting to CXOs, mentoring data engineering teams, and performing 'Vendor/Tool Evaluations' for GenAI.</li></ul>
