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Kale Logistics - Senior DevOps Engineer - Kubernetes/Azure Kubernetes Service

Kale Logistics Solutions
6 - 10 Years
Pune

Posted on: 30/10/2026

Job Description

Role & responsibilities :

- Design and maintain CI/CD pipelines for automated builds, testing, deployments, and releases.

- Manage Docker, Kubernetes, Azure Kubernetes Service (AKS), and Linux infrastructure.

- Automate infrastructure provisioning and configuration using Terraform, Ansible, or equivalent tools.

- Support customer deployments across UAT, PROD, HA, and DR environments.

- Troubleshoot networking, DNS, TLS certificates, firewalls, connectivity, and deployment issues.

- Establish observability across infrastructure and application services using centralized logging, metrics, distributed tracing, and alerting.

- Integrate security scanning and vulnerability remediation, secret management, and access controls into the deployment pipeline.

- Collaborate with Engineering, QA, Security, and customer IT teams to ensure deployment readiness and timely go-lives.

- Create reusable deployment automation, operational documentation, and runbooks.

Preferred candidate profile :

- Strong experience with CI/CD tools such as GitHub Actions, Azure DevOps, or Jenkins.

- Hands-on expertise in Docker, Kubernetes, AKS, and Helm.

- Experience with Azure cloud, Linux, Infrastructure as Code, and scripting (Bash/Python).

- Hands-on experience with observability tools and practices, including Prometheus, Grafana, centralized logging, and distributed tracing.

- Working knowledge of PostgreSQL, MongoDB, backups, and recovery.

- Knowledge of DevSecOps, vulnerability scanning, and security best practices.

Workload & Ingress Management :

- Configuring networking, CoreDNS, Ingress/Gateway API controllers, and Network Policies.

Preferred Qualifications :

- Experience with enterprise customer deployments in cloud and on-premises environments.

- Knowledge of HA/DR architecture, security assessments, and compliance requirements.

- Basic understanding of AI/ML deployment concepts, LLM-based applications, and model-serving endpoints.

- Exposure to deploying AI services using tools such as Ollama or cloud-based AI platforms.

- Familiarity with AI coding assistants and AI-powered tools for DevOps automation and troubleshooting.

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