Senior Azure Platform & DevOps Engineer
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Salary
≈ $70k – $194k per year (Estimated)
Location
Hybrid (Tel Aviv, Israel)
Seniority
Senior · 5+ years exp
Confirmed on the employer's own hiring board on Oct 17, 2026. First seen by Alion on Jun 15, 2026. Gini-Apps scores B on the Alion truth index.
Overview
Company
Impact
Profile match
Gini-Apps is a leading provider of custom software development, mobile and web app development, UX/UI, QA, AI, outsourcing & placement services. We deliver innovative, high-quality digital solutions tailored to your business needs. Let’s build something great together.
Description
A fast-growing enterprise technology company is looking for a Senior Azure Platform & DevOps Engineer to join its core engineering team and take full ownership of its Azure cloud environment. The role is highly hands-on and involves designing, building, and operating a production-grade platform that supports AI agents, Kubernetes-based workloads, and large-scale workflow orchestration, with close collaboration alongside backend and AI engineers.
Responsibilities
- Own the end-to-end Azure platform and ensure production reliability and scalability
- Design and evolve cloud architecture supporting AI-driven, Kubernetes-based workloads
- Build and maintain Infrastructure as Code and deployment automation
- Lead CI/CD and GitOps processes across environments
- Operate and improve production Kubernetes systems and observability stack
- Drive security, governance, and cost optimization across the platform
Requirements
- 5+ years of experience in DevOps / Platform / Infrastructure engineering
- Strong hands-on experience with Azure (AKS, Networking, Entra ID, Key Vault, Storage)
- Deep Kubernetes expertise in production environments
- Strong experience with Terraform or OpenTofu
- Experience with CI/CD pipelines (Azure DevOps preferred)
- Experience with GitOps tools (ArgoCD or Flux)
- Strong knowledge of PostgreSQL and cloud databases
- Ability to work independently with high ownership
Nice to Have
- Experience with Temporal or workflow orchestration systems
- Experience with AI/ML infrastructure or model serving
- Experience with Azure AI services (OpenAI, AI Search)
- Experience optimizing Kubernetes workloads for GPU/AI systems
- Experience with observability tools such as Langfuse or similar
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