Senior Java Engineer (Kuala Lumpur, Malaysia) Malaysia| Permanent, Full time| Amethyst Partners[...]

Amyethyst Asia Partners; Kuala Lumpur; On-site; MYR 90,000 - 120,000; Full time

Job summary

A leading technology firm in Kuala Lumpur, Malaysia, is seeking a Senior Java Engineer to drive backend architecture for a rapidly scaling global platform. You will lead the design of distributed systems, optimize cloud-native microservices, and integrate AI-powered features. Ideal candidates will have a strong background in Java and microservices, with a passion for solving complex engineering challenges in high-traffic environments.

Qualifications

• Proven experience in backend engineering and architecture design.
• Strong understanding of distributed systems and microservices.
• Experience with AI-assisted coding tools is a plus.

Responsibilities

• Lead architecture design for large-scale, high-concurrency distributed systems.
• Build and optimize cloud-native microservices.
• Integrate AI-powered features and resolve high-traffic challenges.

Skills

Java

Microservices

Kubernetes

AI integration

Cloud-native design

Tools

Kubernetes

Spring Cloud Alibaba

Redis

Kafka

Job description

Our client is looking for a Senior Java Engineer to drive backend architecture and high-performance distributed systems for a rapidly scaling global platform. This role is ideal for engineers who thrive in large-traffic environments, enjoy solving complex system challenges, and want to work at the forefront of AI-assisted engineering.

You will take ownership of system design for applications serving tens of millions of users, while shaping engineering standards, performance strategy, and next-generation AI integrations.

Key Responsibilities

• Lead architecture design for large-scale, high-concurrency distributed systems.
• Build and optimize cloud-native microservices using Kubernetes, Spring Cloud Alibaba, and modern reliability strategies.
• Integrate AI-powered refactoring, feature enhancement, and workflow improvements.
• Design and implement AI application-layer features (e.g., RAG systems, recommendation logic).
• Resolve high-traffic challenges related to performance, scalability, and resilience.
• Improve distributed storage systems (Redis, Kafka) and strengthen microservice governance.
• Enhance engineering efficiency by introducing best practices, tools, and automation.
• Use AI-assisted coding tools to improve productivity and CI/CD pipeline quality.