About this role
Role Mission: The Senior DevOps Engineer will be involved in the end-to-end delivery lifecycle including solution design, application development, QC testing, CI/CD automation and continuous optimization of cloud-native platforms. The candidate will need to manage Kubernetes operations, infrastructure provisioning and software upgrades to ensure systems remain reliable, secure and scalable. The candidate will be working in a team in routine on-call rotations to support day-to-day operations, incident resolution, customer care and defect fixes. Key Responsibilities Design and Development: Design, develop, and maintain robust, scalable, and resilient microservices using Java, Spring Boot and related frameworks. Hands-on experience with Apache Camel and its core Enterprise Integration Patterns (EIPs). Implement RESTful APIs and ensure services meet performance, security, and stability requirements. Write clean, well-tested, and efficient code following best practices and design patterns. Containerization and Deployment: Develop and manage services deployed on OpenShift/Kubernetes, including defining Dockerfiles and Kubernetes resource configurations (Deployments, Services, ConfigMaps, etc.). Work with CI/CD pipelines (e.g., Jenkins) for automated build, test, and deployment. Monitor and troubleshoot services running in the OpenShift environment. Architecture and Data: Contribute to the architectural design of microservices, focusing on principles like loose coupling, high cohesion, and fault tolerance. Implement various data persistence technologies, including relational (e.g., PostgreSQL) and NoSQL databases (e.g., MongoDB, Redis). Integrate services using messaging systems (e.g., Red Hat AMQ) where required. Collaboration and Quality: Perform code reviews to maintain code quality and share knowledge within the team. Ensure compliance with security standards and industry regulations by performing security vulnerability scans (SAST, DAST, SCA) and disaster recovery exercise. Support on-call duty on shift rotation and handle production incident and root cause analysis with minimal supervision. Knowledge Management: Build technical documentation from scratch, maintaining working documents on architecture overviews, API specifications and system integrations. Create and maintain operational runbooks to support the team and internal stakeholders. AI Competency: Familiar with or have experience in agentic SDLC with competencies in prompt and context engineering, agent orchestration and supervision, and AI assisted delivery. Automate data gathering, pattern finding, auto fixing or self-healing with anomaly detection and analysis.