Responsibilities : - Design and build scalable, reliable backend systems that power core enterprise platforms.- Develop and maintain microservices and event-driven architectures, ensuring systems are loosely coupled and highly available.- Take ownership of end-to-end system development, from design to deployment and production support.- Ensure systems are production-ready by implementing strong observability, monitoring, and alerting mechanisms.- Optimize system performance by improving latency, throughput, and resource utilization.- Collaborate with cross-functional teams to define APIs, service contracts, and data models.- Implement resilience patterns such as retries, circuit breakers, and graceful degradation.- Contribute to and improve CI/CD pipelines and deployment processes for faster and safer releases.- Write clean, maintainable, and well-tested code, following best engineering practices.- Use production data and metrics to continuously improve system reliability and performance.- Participate in code reviews, technical discussions, and architectural decisions.- Support and mentor team members, contributing to overall engineering excellence.- Explore opportunities to integrate automation or AI-driven enhancements where https://jobeax.com/link/BHZrlmjQPZniL0H3 : - Strong expertise in Java (Spring Boot) and working knowledge of Kotlin for building scalable backend services.- Deep understanding of distributed systems design, including scalability, fault tolerance, and eventual consistency.- Hands-on experience with microservices architecture and event-driven systems using messaging platforms like Kafka.- Proficiency in working with both relational databases (PostgreSQL, Oracle) and NoSQL databases (MongoDB, Cassandra, Redis), including data modeling and performance optimization.- Experience with API design, service contracts, and building high-throughput, low-latency services.- Familiarity with cloud-native development in environments like AWS, GCP, or Azure.- Strong experience in containerization (Docker) and orchestration tools like Kubernetes.- Knowledge of CI/CD pipelines, automated testing, and deployment strategies (blue-green, canary releases).- Understanding of observability practices, including logging, monitoring, and distributed tracing.- Exposure to search technologies like ElasticSearch.- Solid foundation in data structures, algorithms, and problem-solving.- Working knowledge of secure coding practices and authentication/authorization mechanisms.- Exposure to AI-assisted development tools and basic understanding of integrating AI-driven components.- Leverage AI coding assistants for development and utilize them to generate better quality output.- Support integration of AI- or automation-driven components into the backend systems.- Ensure AI-enabled components operate within defined reliability and safety boundaries.- AI exposure is valued, but distributed systems engineering remains the primary focus of this role. (ref:hirist.tech)