Live opening · Posted 27 days ago
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Responsibilities:
Design and develop scalable, high-performance microservices for international eCommerce returns workflows using Java/J2EE and Spring Boot.
Architect and implement event-driven processing pipelines using Kafka for asynchronous, decoupled communication between services.
Build and maintain RESTful and gRPC APIs following best practices for reliability, versioning, and backward compatibility.
Design and optimise data models across relational (SQL) and non-relational (NoSQL) databases to support complex returns workflows.
Apply design patterns (SOLID, DDD, CQRS, Saga) to ensure code is maintainable, extensible, and production-ready.
Leverage AI-powered development tools (e. g., GitHub Copilot, Wibey, ChatGPT) to accelerate code generation, debugging, code reviews, test writing, and documentation.
Collaborate with architects and senior engineers on system design, technical roadmaps, and capacity planning.
Ensure high availability and fault tolerance through proactive monitoring, alerting, and incident response.
Integrate with upstream and downstream systems (order management, logistics, payments) via messaging and APIs.
Participate in agile ceremonies: sprint planning, grooming, and retrospectives, contributing to team velocity and quality.
Requirements:
6 to 10 years of total experience with a strong Java backend focus; minimum 5+ years in backend engineering platform development.
5+ years of experience in Java technologies (Java 17+), Spring Boot, distributed systems, and large-scale application development and design.
Hands-on experience with Apache Kafka, SQL/NoSQL databases (Azure SQL, Cosmos DB, or equivalent).
Experience with microservices architecture, service decomposition, API contracts, circuit breakers, and service discovery.
Proficiency in applying Gang-of-Four, enterprise integration, and domain-driven design patterns; experience with event-driven design.
Ability to lead and contribute to system design discussions, capacity planning, trade-off analysis, scalability, and fault tolerance.
Proficiency in using AI coding assistants (GitHub Copilot, ChatGPT, Claude, or equivalent) for code generation, refactoring, test scaffolding, PR reviews, and documentation with demonstrated ability to critically evaluate AI-generated output for correctness, security, and performance.
Understanding of prompt engineering patterns (chain-of-thought, few-shot, system prompts) to get reliable, high-quality outputs from LLMs.
Experience with containerization technology (Kubernetes/WCNP) and CI/CD pipelines.
Experience
5-9 yrs
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