Backend Engineer - AI Platform and Cloud Native Services

Lenovo•Morrisville, NC

2d•$152,000 - $233,105•Remote

About The Position

Lenovo is building a team focused on creating a solution for managing and optimizing token spend and other enterprise AI services. This platform will offer model orchestration, token accounting, cost controls, security governance, usage analytics, and API management. It aims to enable organizations to securely scale AI adoption while maintaining visibility, compliance, and budget control across diverse AI workloads and ecosystems.

Requirements

Bachelor's degree or above in Computer Science, Software Engineering, or equivalent experience.
5-10+ years of experience with high-scale distributed systems and cloud-native development.
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.
AI-assisted development tools proficiency is required.
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.

Nice To Haves

Exposure to NVIDIA AI ecosystem technologies (NIMs, Triton Inference Server, Metropolis, DeepStream) is a plus.

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.