Senior AI Solution Architect

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.'

• Experience building multi-step workflows where an AI agent uses APIs (tools) to complete a task, rather than just generating text.
• Candidates who have experience with 'Model Drift' detection and automated retraining pipelines (MLOps).

Vertex AI & MLOps Lifecycle

This is a GCP-centric role, requiring expertise in the Vertex AI suite.

• 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.

• 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.

• 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.

• Experience presenting to CXOs, mentoring data engineering teams, and performing 'Vendor/Tool Evaluations' for GenAI.