Senior AI Solution Architect

About the role

This is a senior-level position requiring 10–15+ years of experience. The role combines deep GCP technical expertise with the emerging field of Agentic AI, requiring a visionary who can bridge business ROI and technical implementation.

Agentic AI & Orchestration Frameworks

Build systems that move beyond basic chatbots and can "think" and "act" using autonomous agents.

• Key technologies: Vertex AI Agent Builder, LangChain, LangGraph, CrewAI, AutoGen
• Build multi-step workflows where an AI agent uses APIs (tools) to complete tasks, rather than just generating text

Vertex AI & MLOps Lifecycle

Expert-level command of the Vertex AI suite with demonstrated ability to productionize models.

• Key technologies: Vertex AI Pipelines, Model Registry, Feature Store, Model Monitoring, CI/CD for ML
• Implement model drift detection and automated retraining pipelines (MLOps)

GCP Data Lakehouse Architecture

Deep understanding of storing and processing massive datasets that fuel AI.

• Key technologies: BigQuery (specifically BigQuery ML and BigLake), Dataproc, Dataflow, Medallion Architecture (Bronze/Silver/Gold)
• Unify data lakes (unstructured storage) with data warehouses (structured SQL) into a single Lakehouse on GCP

Generative AI & RAG (Retrieval-Augmented Generation)

Architect solutions using LLMs like Gemini, with the ability to ground models in company-specific data.

• Key technologies: Gemini (Pro/Flash), Vector Databases (Vertex AI Search & Conversation), Prompt Engineering, Embeddings
• Build RAG architectures where an LLM retrieves real-time data from a database to provide accurate, non-hallucinated answers

Cross-Functional Technical Leadership

Senior visionary who bridges business ROI and technical implementation.

• Key areas: Reference Architectures, Stakeholder Management, Solution Blueprints, Cost Optimization (FinOps)
• Present to CXOs, mentor data engineering teams, and perform vendor/tool evaluations for GenAI