Cloud Solution Architect (GenAI & GCP Focus)

Role Summary

Senior Cloud Solution Architect responsible for designing enterprise-scale cloud and AI platforms, defining architecture standards, and leading adoption of GenAI, Agentic AI, and MLOps capabilities across the organization.

Key Responsibilities

• Lead architecture design for cloud-native and AI-powered solutions.
• Define HLD/LLD documentation, security, networking, IAM, HA/DR, and operational models.
• Design scalable AI infrastructure and AI serving architectures.
• Define enterprise standards for cloud, AI, security, and platform operations.
• Design GenAI, RAG, and Agentic AI solutions for business use cases.
• Define MLOps and LLMOps architecture and governance standards.
• Guide engineering teams through implementation and adoption.
• Support AI strategy, roadmap development, and technology selection.

Required Skills

Cloud Architecture

• 8+ years of experience in cloud or software development.
• 3+ years specifically as a Cloud Architect, with a focus on GCP designing and implementing complex production environments.
• Proven experience as a Senior DevOps, SRE, or System Engineer.
• At least 3 years of hands-on experience with Terraform and Kubernetes.

AI Infrastructure & Security Architecture

• Experience designing enterprise AI/ML and GenAI platforms.
• Understanding of AI security architecture, model governance, data protection, and compliance.
• Experience designing scalable AI infrastructure and AI serving platforms.

Agentic AI Architecture

• Experience designing agent-based and multi-agent systems.
• Knowledge of modern agent frameworks and orchestration platforms (Google ADK, LangGraph, CrewAI, AutoGen, or similar).
• Ability to define standards for agent lifecycle, governance, and operational support.

Agent-to-Agent Communication Architecture (ATC)

• Design communication patterns for multi-agent ecosystems using Agent-to-Agent (A2A/ATC) protocols.
• Define agent interaction models, trust boundaries, permissions, governance, and security controls.
• Establish standards for agent interoperability, orchestration, observability, and scalability.

AI Serving & LLM Platforms

• Experience designing AI inference, model serving, and GenAI platform architectures.
• Understanding of performance, scalability, resilience, and cost optimization for AI workloads.

MLOps Architecture

• Experience defining enterprise MLOps strategies.
• Experience with RAG architectures, vector databases, embeddings, and LLM integration patterns.

Nice to Have

• Google Professional Cloud Architect certification.

What We Offer

• Hybrid work model: 4 days onsite in Rishon LeZion, 1 day remote.
• Design and own the architecture of AI platforms and cloud solutions.
• Drive technical standards and architectural decisions across the organization.