Senior AI Solution Architect

JD Below
Senior AI Solution Architect 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.

• 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 .
• 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 GCP-centric role, the candidate must be an expert in the
Vertex AI suite. They need to demonstrate they can product ionize models, not just build them.

• Key /CD for ML.
• What to look for:
  
  Candidates who have experience with ‘Model Drift’ detection and automated retraining pipelines (MLOps).
• GCP 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  (specifically Big Query ML and Big Lake), 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 GCP.
• 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  (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 1015 year level, this person is a ‘Senior Visionary.’ They need to bridge the gap between business ROI and technical implementation.

• Key  (Fin Ops).
• What to look for:
  
  Experience presenting to CXOs, mentoring data engineering teams, and performing ‘Vendor/Tool Evaluations’ for GenAI.