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Google Cloud Gen AI Architect

Enterprise AI Value Strategy Team Lead/Consultant | Mid-Level | Full time
Job No. 13702525 | Prague
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Focus: High-level solution design, ecosystem integration, stakeholder strategy, and enterprise-grade deployment on GCP.

Position Overview
We are seeking a Google Cloud Gen AI Architect to lead the technical design and strategy of our AI initiatives. This role requires a blend of hands-on prototyping capability and high-level architectural thinking. You will leverage the full Google Cloud Platform (GCP) ecosystem to design scalable, secure, and cost-effective Generative AI solutions. You will be the bridge between business stakeholders and technical execution, translating complex client needs into robust architectures using Vertex AI and Gemini.

Key Responsibilities

  • Vertex AI Solution Architecture: Design end-to-end architectures for Generative AI applications, selecting the right GCP services (e.g., Vertex AI Agent Builder, Conversation, or Search) vs. custom LangChain implementations.
  • Architectural knowledge & scalability: Apply the Google Cloud Architecture Framework principles to Gen AI workloads. Design systems that ensure High Availability (HA), handle traffic spikes (auto-scaling), and manage latency requirements for real-time user experiences.
  • Client Advisory & Prototyping: Lead technical workshops with clients to define use cases; rapidly build interactive PoCs using Streamlit on Cloud Run or Vertex AI Studio to demonstrate feasibility.
  • Enterprise RAG Design: Architect advanced Retrieval-Augmented Generation (RAG) systems, deciding on optimal storage strategies (e.g., BigQuery Vector Search vs. AlloyDB pgvector vs. Vertex AI Vector Search) based on data volume, throughput, and indexing speed.
  • Agentic Orchestration Strategy: Define the strategy for autonomous agents, determining when to use Gemini Function Calling versus open-source frameworks, and ensuring agents act safely within enterprise constraints.
  • Governance, Security & Cost: Oversee the operational side of LLMs (LLMOps). Implement IAM best practices, manage Vertex AI quotas, design for cost optimization (FinOps), and ensure data residency and compliance.
  • Full-Stack Integration: Guide the integration of LLM logic into wider enterprise systems using Cloud Functions, API Gateway, and Pub/Sub, ensuring loose coupling and event-driven reliability.

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Qualifications & Technical Skills

  • GCP Ecosystem: Deep expertise in the Google Cloud AI stack, specifically Vertex AI (Model Garden, Studio), Document AI, and BigQuery.
  • Architecture Mastery: Proven ability to design cloud-native systems using Microservices and Event-driven patterns. Deep understanding of Distributed Systems, Load Balancing, and State Management in the context of stateless AI APIs.
  • Model Expertise: Extensive experience architecting solutions with the Gemini family (Pro, Flash, Ultra) and understanding their specific context windows, reasoning capabilities, and trade-offs between cost and latency.
  • Vector Database Strategy: Strategic knowledge of Google-native vector solutions (Vertex AI Vector Search) and how to index and retrieve enterprise data effectively at scale.
  • Python & Frameworks: Strong command of Python and deep familiarity with LangChain on Vertex AI and LlamaIndex.
  • Soft Skills: Good presentation skills, capable of explaining "Why Google Cloud AI" to stakeholders and defending architectural decisions.

  • Fluency in English and Czech/Slovak is required. German is an advantage.

Preferred Certifications

We are looking for candidates who validate their expertise through official Google Cloud credentials:

  • PREFERRED:
    • Google Cloud Professional Cloud Architect: Demonstrating your ability to design secure, scalable, and reliable cloud solutions.
    • Google Cloud Professional Machine Learning Engineer: Demonstrating specific expertise in ML pipeline design and model deployment.
  • NICE TO HAVE:
    • Google Cloud Professional Data Engineer: For candidates with strong backgrounds in BigQuery and data pipeline orchestration.

Prague

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