Senior Principal Backend Development Engineer Kuala Lumpur, Malaysia

Responsibilities

• Own end-to-end technical solution design and delivery for company-wide technical initiatives.
• Lead the engineering implementation of AI capabilities, including AI-assisted development, AI code review, AI observability analysis, and AI-driven operations automation.
• Proactively resolve complex technical challenges across business lines, driving improvements in efficiency and quality through technical means.
• Design and optimize high-concurrency, distributed system architectures, and explore AI applications in capacity planning, performance bottleneck analysis, and anomaly detection.
• Provide technical support and training to business teams, promoting the adoption and standardization of AI and engineering capabilities across the organization.

Qualifications

• Core Engineering & Architecture
• Strong coding fundamentals: proficiency in Go or Java concurrent programming, deep understanding of design and optimization in high-concurrency scenarios.
• Distributed systems experience: hands‑on experience with distributed services and distributed storage; familiarity with microservices architecture design.
• System optimization: solid knowledge of Linux OS; understanding of middleware internals and engineering practices (e.g., etcd, Nacos, Kafka).
• Frameworks & protocols: proficiency in gRPC; experience with development and performance tuning is a plus.
• Troubleshooting skills: proficient with profiling tools, Arthas, and Linux CLI for system analysis and issue diagnosis.
• AI engineering mindset: understanding of LLM fundamentals, capability boundaries, and practical engineering usage.
• Preferred: one or more of the following:
• Engineering experience with AI-assisted development / code review tools (e.g., Copilot, Claude, DeepSeek).
• Practical experience applying AI to log analysis, distributed tracing, performance profiling, or anomaly detection.
• Design experience with AI knowledge augmentation (RAG) or tooling systems built on internal data (code, logs, metrics, docs).
• Ability to evaluate the impact of AI capabilities on system stability, security, cost, and compliance from an architectural perspective.
• Clear understanding of AI's role as an "assistive tool" in engineering — avoids over-reliance and demonstrates sound engineering judgment.
• Experience & Contributions
• Bachelor's degree or above; 3+ years of relevant development experience.
• Open-source contributions, or a track record of technical blogging / knowledge sharing is a plus.
• Strong communication and collaboration skills; ability to drive holistic improvements across AI, architecture, and engineering efficiency.

Bonus Points

• Experience designing financial systems, trading systems, or high-availability systems.
• Hands‑on experience with AI-driven observability, automated operations, or stability governance.
• Familiarity with the design and internals of multiple open-source middleware solutions (e.g., Redis, Kafka).
• Experience with microservices governance, service discovery, or configuration management systems.
• Proven solutions or successful case studies in high-performance architecture design or AI engineering implementation.

Benefits

• Study Growth Fund: support for professional development and continuous learning.
• Internal Events: regular team-building activities, workshops, and events designed to promote collaboration and innovation.
• Global Collaboration: work alongside a diverse, international team.
• Career Advancement: opportunities for growth and advancement within a rapidly expanding global company.
• Internal Mobility: long-term development with internal job opportunities to build your career path.