Google GenAI Architect

Extensive experience with Google Cloud Platform (GCP), including demonstrated expertise in AI/ML solution architecture and leading enterprise-scale implementations

Role Overview

We are seeking a visionary Google GenAI Architect to lead the design and implementation of cutting-edge intelligent systems and Generative AI solutions on Google Cloud. You will be the technical authority responsible for translating business challenges into sophisticated AI-powered applications. You will architect enterprise-grade GenAI solutions, Retrieval-Augmented Generation (RAG) pipelines, and autonomous agent workflows by harnessing the full power of Google's AI stack, including Vertex AI and Gemini.

Key Responsibilities

• GenAI Solution Design: Architect end-to-end enterprise AI applications, from proof-of-concept to production, leveraging Vertex AI, Gemini models, and Model Garden.
• RAG & Vector Search: Design and deploy scalable, low-latency enterprise Retrieval-Augmented Generation (RAG) systems using Vertex AI Vector Search, embedding models, and advanced document grounding techniques.
• Agentic Workflows & Tooling: Architect sophisticated autonomous AI agents, tool-calling pipelines, and multi-agent systems using frameworks such as LangChain, LlamaIndex, or the Google GenAI SDK.
• AI Governance & MLOps: Implement robust frameworks for model evaluation, prompt engineering, context optimization, and performance monitoring. Champion responsible AI by implementing safety filters and guardrails.
• AI-Ready Infrastructure: Collaborate with cloud infrastructure teams to define the optimal GKE, networking, and data configurations required to support high-performance AI/ML workloads.
• Technical Leadership: Lead innovation in the AI space, mentor engineers on GenAI best practices, and translate complex AI concepts into clear architectural roadmaps and high-level designs (HLD).

Required Qualifications & Technical Skills

• AI/GenAI Stack: Expert-level architectural experience with Vertex AI Studio, Gemini API, Vector Search, BigQuery ML, and prompt engineering/RAG patterns.
• Core GCP Foundation: Proven experience with the GCP services that underpin modern AI solutions, including GKE, Cloud Run, Pub/Sub, and BigQuery.
• Programming & Automation: Advanced proficiency in Python for AI/ML development and strong skills in SQL.
• Architecture Frameworks: Deep understanding of distributed systems, MLOps, and the unique architectural patterns required for building and scaling LLM-based applications.
• Education: Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or a related field.

Preferred Certifications

• Google Cloud Certified Professional Machine Learning Engineer (Strongly Preferred)
• Google Cloud Certified Professional Cloud Architect (Strong Advantage)