DevOps Engineer

We are seeking a hands-on Senior DevOps Engineer to build and operate secure, reliable cloud infrastructure and delivery pipelines for enterprise applications, including AI-enabled services. The ideal candidate will demonstrate strong automation skills, production ownership, a stable employment history, continuous professional growth, and excellent client-facing communication., * Minimum 7 years of relevant engineering experience, including substantial hands-on DevOps, cloud, and automation experience. \n

• Strong experience with AWS, Azure, or Google Cloud. \n

• Hands-on experience with Docker and Kubernetes. \n

• Infrastructure as Code experience using Terraform or an equivalent tool. \n

• Strong CI/CD experience with Jenkins, Azure DevOps, GitHub Actions, or GitLab CI. \n

• Proficiency in Linux administration, Git, and scripting using Python, Bash, or PowerShell. \n

• Working knowledge of networking, DNS, TLS, load balancing, and cloud access controls. \n

• Experience with monitoring and logging platforms such as Prometheus, Grafana, OpenTelemetry, or cloud-native equivalents. \n

• Experience with production troubleshooting, incident management, release controls, and recovery procedures. \n

• Practical experience supporting AI-enabled applications or deploying AI services, including secure credentials, observability, and cost controls. \n

• Practical use of GitHub Copilot, Cursor, ChatGPT, or Claude for engineering and automation. \n

• Stable employment history with sustained contributions and continuous growth in skills, responsibilities, or leadership. \n

• Excellent verbal and written communication, strong interpersonal skills, and a pleasant, professional demeanor. \n

• Ability to communicate operational risks clearly and collaborate effectively with client stakeholders., * Experience in logistics, transportation, or supply-chain environments. \n

• Experience with Helm, Argo CD, GitOps, and configuration management. \n

• Exposure to MLOps/LLMOps, model registries, evaluation pipelines, and model-serving infrastructure. \n

• Experience with cloud cost optimization and capacity planning.

• Implement secrets management, identity and access controls, vulnerability scanning, and security checks in delivery pipelines. \n

• Support deployment and operation of AI services, model endpoints, and supporting data or retrieval infrastructure. \n

• Manage versioning and controlled releases of AI application configurations, prompts, and models where applicable. \n

• Integrate automated AI evaluations into release pipelines and monitor latency, failures, usage, and operating costs. \n

• Use AI assistants to support scripting, infrastructure development, troubleshooting, and incident analysis, independently validating generated recommendations. \n

• Maintain disaster-recovery procedures, operational runbooks, and deployment documentation. \n

• Collaborate with development, QA, security, and client teams and mentor engineers. \n

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