Associate AI Engineer

The Lenovo AI Technology Center (LATC), the Center of AI Excellence for Lenovo, is driving our transformation into an AI-first organization. We are assembling a world-class team of researchers, engineers, and innovators to position Lenovo and its customers at the forefront of the generational shift toward AI. Lenovo is one of the world's leading computing companies, delivering products across the entire technology spectrum, spanning wearables, smartphones (Motorola), laptops (ThinkPad, Yoga), PCs, workstations, servers, and services/solutions. This unmatched breadth gives us a unique canvas for AI innovation, including the ability to rapidly deploy cutting-edge foundation models and to enable flexible, hybrid-cloud, and agentic computing across our full product portfolio. To this end, we are building the next wave of AI core technologies and platforms that leverage and evolve with the fast-moving AI ecosystem, including novel model and agentic orchestration & collaboration across mobile, edge, and cloud resources. This space is evolving fast and so are we. If you're ready to shape AI at a truly global scale, with products that touch every corner of life and work, there's no better time to join us.

Summary:

Lenovo is seeking a highly motivated Associate AI Engineer to contribute to the design, development, and exploration of our next-generation AI systems. As an Associate AI Engineer, you will help build and scale the AI inference platform that powers Lenovo's personal AI assistant Qira, where you will work with cross-functional teams contributing to the development of backend services, API endpoints, and inference workflows that enable seamless integration of foundation models into real-world user experiences.

In this role, you will support the design and implementation of scalable AI services, develop and maintain APIs for model access and orchestration, and leverage agentic AI tooling and workflow frameworks to connect models with enterprise systems, tools, and data sources. You will help optimize inference performance, improve system reliability, and contribute to the infrastructure that enables Qira to deliver intelligent, responsive, and personalized AI experiences at scale.

This is an exciting opportunity to gain hands-on experience with cutting-edge AI systems while collaborating with experienced engineers, researchers, and product teams to help advance Lenovo's Hybrid AI vision and make Smarter Technology for All.

Key Responsibilities

• Build agentic AI workflows - Design and implement multi-step AI agents that can plan, call tools/APIs, reason over intermediate results, and complete complex tasks end-to-end.

• Build AI inference services - Develop and maintain scalable backend services that power Qira's AI experiences, enabling fast, reliable, and efficient model inference across a variety of use cases and devices.

• Develop APIs and service layers - Design, implement, and optimize API endpoints and service interfaces that connect applications, AI models, data sources, and platform components, ensuring secure and high-performance integrations.

• Diagnose and resolve issues across the stack - Investigate defects, performance bottlenecks, and reliability issues in the model-service pipeline, performing root-cause analysis that often spans process boundaries and specific hardware architectures (AMD, Intel, NVIDIA, ARM), and verifying fixes on the affected configurations.

• Optimize inference performance and reliability - Contribute to model serving, request routing, caching, monitoring, and performance optimization strategies to improve latency, throughput, scalability, and overall user experience.

• Leverage agentic AI tooling and workflows - Build and integrate agentic workflows that enable foundation models to access tools, enterprise systems, and external services, helping deliver intelligent and actionable responses within Qira.

• Collaborate across teams and productionize solutions - Partner with AI researchers, platform engineers, product managers, and software developers to take AI capabilities from proof-of-concept to production-ready services, with strong focus on observability, testing, and operational excellence.

• Engineer for reliability and safety - Contribute to guardrails, monitoring, and fallback strategies so agentic systems behave predictably and safely in real user environments.

• Collaborate across disciplines - Work closely with engineering, research, and platform teams to understand real use cases and ship features that solve concrete user problems.

• Continuously learn new tools & frameworks - Ramp quickly on new agent frameworks, libraries, and infrastructure as the ecosystem evolves.

Qualifications

• Bachelor's or Master's degree in Computer Science, Computer Engineering, or a closely related technical field (e.g., EE, Math, Statistics, Physics) with a strong computing/programming focus.

• Programming experience in any scripting language, including Python, Rust, Java, Kotlin, C, or C++.

• Solid CS fundamentals: data structures, algorithms, complexity, concurrency.

• Experience building and debugging non-trivial software systems (class projects, internships, research code, or open source).

• Coursework or project experience in machine learning or deep learning (e.g., supervised learning, optimization, neural networks).

• Experience using LLMs via APIs (e.g., OpenAI, Anthropic, etc.) or open-source models for a project, internship, or research.

• Demonstrated ability to quickly learn new tools, libraries, and frameworks.

• Strong analytical and debugging skills; comfortable working with incomplete or noisy real-world data.

• Strong written and verbal communication skills with an emphasis on creating clear documentation oriented to both business and developer needs.

• Ability to work effectively in a team setting: code reviews, design discussions, and cross-functional collaboration.

Bonus Points

• Coding experience in Rust

• Experience troubleshooting issues across development, testing, and production environments, investigating root causes, resolving customer and internal support escalations,

• Exposure to software validation across diverse hardware and platform architectures, including AMD, Intel, ARM, and NVIDIA devices to ensure compatibility, performance, and consistent user experiences.

• Support engineering experience triaging requests, reproducing bugs, identifying root causes and recommending fixes.

• Backend experience: building APIs/services, working with cloud platforms and containerization (e.g., Docker), and basic telemetry tools for logging, monitoring, or tracing (e.g, OpenTelemetry).

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