Cloud Engineer / DevOps Engineer (GenAI & GCP Focus)

Role Summary

Cloud Engineer responsible for building, automating, securing, and operating cloud platforms on GCP, with a focus on AI infrastructure, Agentic AI, AI serving, and MLOps capabilities.

Core Responsibilities

• Developing and maintaining cloud architecture (Landing Zones), procedures, and policies for proper cloud utilization in GCP.
• Guiding development and DevOps teams in selecting appropriate services and solving complex architectural issues.
• Writing and managing high-level, modular infrastructure as code for organization-wide use.
• Monitoring and controlling cloud costs, identifying waste, and recommending optimization actions such as right-sizing and RI/Savings Plans.
• Working closely with security teams to implement automated controls and improve the overall security posture.
• Design scalable AI infrastructure, AI serving architectures, GenAI, RAG, and Agentic AI solutions. Define enterprise standards for cloud, AI, security, and platform operations; MLOps and LLMOps architecture and governance standards. Support AI strategy, roadmap development, and technology selection.

 Required Skills

Cloud & Platform Engineering

• 5+ years of Cloud / DevOps Engineering experience.
• Strong hands-on experience with GCP.
• Terraform and Infrastructure as Code expertise.
• Kubernetes, Docker, and cloud-native platforms.
• Python and automation scripting.
• CI/CD platforms (GitHub Actions, GitLab CI, Jenkins, or similar).
• Cloud networking and security fundamentals.

AI Infrastructure & Security

• Experience supporting AI/ML workloads in production environments.
• Understanding of AI platform security, IAM, data protection, and governance principles.
• Experience operating scalable AI infrastructure and cloud-native AI services.

Agent Development (ADK)

• Experience building AI agents and agentic workflows using modern frameworks (Google ADK, LangGraph, CrewAI, AutoGen, or similar).
• Integration of AI agents with enterprise systems, APIs, tools, and workflows.

Agent-to-Agent Communication (ATC)

• Experience implementing Agent-to-Agent (A2A/ATC) communication patterns.
• Understanding of agent orchestration, context sharing, tool calling, and workflow coordination between multiple agents.

AI Serving

• Experience deploying and operating AI inference workloads and model-serving environments.
• Understanding of scalability, observability, performance optimization, and reliability for AI services.

MLOps / LLMOps

• Understanding of MLOps and LLMOps concepts.
• Experience with model deployment, monitoring, experimentation, and lifecycle management.
• Familiarity with RAG architectures, vector databases, embeddings, and LLM integration patterns.

Nice to Have

• Google Professional Cloud Engineer certification.

What We Offer

• Hybrid work model: 4 days onsite in Rishon LeZion, 1 day remote.
• Work with Generative AI and Agentic AI technologies.
• Participate in the design, implementation, and optimization of services and platforms.