Senior Principal Backend Development Engineer

Responsibilities

Lead end-to-end technical solution design and drive implementation across the organization

Spearhead AI engineering adoption (AI-assisted development, AI code review, AI-powered observability, AI-driven operations automation, etc.)

Proactively resolve complex technical challenges across business lines, improving efficiency and quality through technical innovation

Design and optimize high-concurrency, distributed system architectures; explore AI applications in capacity planning, performance bottleneck analysis, and anomaly detection

Provide technical guidance and training to business teams, driving the adoption and standardization of AI + engineering capabilities

Requirements

Must-Have

I. Core Engineering & Architecture

Proficient in Go or Java concurrent programming with deep understanding of high-concurrency system design and optimization

Hands-on experience with distributed services and distributed storage; familiar with microservice architecture design

Solid knowledge of Linux OS and system-level understanding of middleware (etcd, Nacos, Kafka)

Expert-level proficiency in gRPC framework with development and performance tuning experience

Skilled in using profiling tools, Arthas, and Linux command-line utilities for system analysis and troubleshooting

II. AI & Intelligent Systems

AI engineering mindset with understanding of LLM fundamentals, capability boundaries, and practical engineering applications

Ability to evaluate AI's impact on system stability, security, cost, and compliance from an architectural perspective

Clear understanding of AI's "assistive role" in engineering — avoiding over-reliance while leveraging its strengths

III. Experience & Education

Bachelor's degree or above

3+ years of relevant development experience

Strong communication skills and collaborative spirit

Preferred Qualifications

Hands-on experience implementing AI-assisted development/code review (Copilot, Claude, DeepSeek, etc.) at the engineering team level

Practical application of AI in log analysis, distributed tracing, performance profiling, and anomaly detection

Design experience with AI knowledge augmentation (RAG) or tooling systems based on internal data (code, logs, metrics, documentation)

Open-source project contributions

Sustained technical blog or knowledge-sharing track record

Nice-to-Have

Experience designing financial systems, trading systems, or high-reliability systems

AI-driven observability, automated operations, or stability engineering experience

Deep familiarity with open-source middleware internals (Redis, Kafka, etc.)

Microservice governance, service discovery, and configuration center design experience

Proven track record in high-performance architecture design or AI engineering implementation

Core Competency Matrix

Technical Skills (High Priority): Go/Java concurrency, distributed architecture, middleware, gRPC, performance tuning

AI Engineering (High Priority): LLM understanding, AI-assisted development adoption, RAG/tooling design

Project Experience (High Priority): High-concurrency systems, distributed storage, microservice architecture

Problem Solving (High Priority): Profiling, Arthas, Linux command-line troubleshooting

Communication (Medium Priority): Technical training, cross-team alignment, solution evangelism

Industry Background (Medium Priority): Finance/trading/high-reliability systems

Role Characteristics

Core Positioning: A hybrid AI + Architecture role that requires not only traditional distributed systems expertise but emphasizes AI engineering adoption capabilities.

Key Differentiators:

Must lead AI capability implementation at the engineering level — not merely use AI tools

Must evaluate AI's impact on systems from an architectural perspective (stability, security, cost, compliance)

Must drive AI + engineering capability adoption and standardization across teams

Interview Focus Areas

Distributed Architecture: High-concurrency design, microservice architecture, middleware selection and tuning

AI Engineering: Depth of LLM understanding, AI implementation experience, awareness of AI limitations

Problem Solving: Complex troubleshooting experience, performance optimization case studies

Driving Adoption: Technical solution advocacy experience, cross-team collaboration skills