Backend Engineer - AI Platform and Cloud Native Services

Lenovo is building an enterprise AI services platform for token cost management, model orchestration, security governance, usage analytics, and API management. The Backend Engineer will design, develop, maintain, and optimize scalable cloud-native microservices, while integrating AI/ML services and LLM-based workflows into enterprise applications.

Responsibilities:

• Design and develop platform business functions based on Product Requirement Documents (PRD)
• Implement scalable microservices using Java and Spring ecosystems
• Ensure high-performance, reliable backend services that meet business requirements
• Design efficient business models and data structures based on platform requirements
• Collaborate with database teams to optimize PostgreSQL schema design
• Implement best practices for data integrity and system scalability
• Diagnose and resolve platform issues including service exceptions and interface failures
• Utilize logging, monitoring, and debugging tools for rapid problem resolution
• Ensure platform stability and reliability through proactive maintenance
• Analyze and optimize platform performance through code improvements and database tuning
• Configure and optimize middleware components including RabbitMQ and Redis
• Implement monitoring and continuous performance improvements
• Experience integrating AI/ML services, inference APIs, or LLM-based workflows into enterprise applications
• Familiarity with REST/gRPC integration patterns for AI services and GPU-accelerated environments
• Exposure to NVIDIA AI ecosystem technologies (NIMs, Triton Inference Server, Metropolis, DeepStream) is a plus

Requirements:

• Bachelor's degree or above in Computer Science, Software Engineering, or equivalent experience
• 5+ years of experience with high-scale distributed systems and cloud-native development
• AI-assisted development tools proficiency is required
• Strong programming skills including multithreading, concurrency, and profiling and optimization in Java or equivalent
• Expertise  in  common components: PostgreSQL, RabbitMQ, Redis, Docker, Kafka, and Kubernetes
• Experience with CI/CD pipelines and DevOps practices
• Experience with modern software architecture such as real-time event-driven architectures, micro-services and streaming data pipelines
• Familiarity with handling telemetry, sensor, IoT, or operational event data at scale
• Experience integrating enterprise platforms, third-party APIs, and operational systems into unified backend services
• Exposure to NVIDIA AI ecosystem technologies (NIMs, Triton Inference Server, Metropolis, DeepStream) is a plus