Senior AI Solution Architect

About the role

We are seeking a Senior AI Solution Architect to lead the design and implementation of advanced AI solutions on Google Cloud Platform (GCP). This role requires a visionary leader who can bridge business ROI with technical execution, architecting agentic AI systems, production-grade MLOps pipelines, and unified data lakehouse architectures. You will work at the intersection of generative AI, big data, and enterprise orchestration, guiding cross-functional teams and engaging with executive stakeholders to deliver transformative AI capabilities.

Agentic AI & Orchestration Frameworks

• Build multi-step workflows where AI agents use APIs (tools) to complete tasks, moving beyond simple text generation.
• Design autonomous agent systems using frameworks such as Vertex AI Agent Builder, LangChain, LangGraph, CrewAI, or AutoGen.
• Orchestrate complex, stateful interactions between multiple agents and external data sources.

Vertex AI & MLOps Lifecycle

• Productionize machine learning models using the full Vertex AI suite, including Vertex AI Pipelines, Model Registry, Feature Store, and Model Monitoring.
• Implement CI/CD for ML, with automated retraining pipelines and proactive detection of model drift.
• Establish robust MLOps governance to ensure reliability, reproducibility, and performance at scale.

GCP Data Lakehouse Architecture

• Design and unify Data Lakes (unstructured storage) with Data Warehouses (structured SQL) into a single Lakehouse architecture on GCP.
• Leverage BigQuery (including BigQuery ML and BigLake), Dataproc, and Dataflow for large-scale data processing.
• Apply Medallion Architecture (Bronze/Silver/Gold) patterns to organize and refine data assets for AI consumption.

Generative AI & RAG (Retrieval-Augmented Generation)

• Architect solutions using Gemini (Pro/Flash) and other LLMs, grounding models in company-specific data to reduce hallucinations.
• Build RAG architectures that retrieve real-time data from vector databases (Vertex AI Search & Conversation) and enterprise repositories.
• Master prompt engineering, embeddings, and context retrieval strategies to deliver accurate, context-aware responses.

Cross-Functional Technical Leadership

• Develop reference architectures, solution blueprints, and cost-optimization (FinOps) strategies for GenAI workloads.
• Present technical roadmaps and business cases to CXOs and senior stakeholders.
• Mentor data engineering and AI teams, lead vendor/tool evaluations, and align technical decisions with organizational goals.