Development Engineer - Mid Ops Java + AI

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 applicable.

Requirements:

• Mandatory: Java, Kafka, AI, Microservices, Develop REST API, MongoDB, SQL/NoSQL, PostgreSQL, Distributed Systems
• 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 (good to have).
• Leverage AI coding assistants for development and utilize them to generate better quality of 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.