Asocciate AI Engineer

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 design, build, and scale agentic AI systems: multi-step agents, orchestration layers for LLMs and tools, and the surrounding infrastructure that lets foundation models safely interact with real products and users. 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.

• Develop orchestration layers
  Implement services that route and coordinate calls between foundation models, tools, data sources, and other agents (e.g., planners, executors, evaluators).

• Design context and memory management
  Help design and implement strategies for managing context and "memory" across interactions (e.g., chunking, retrieval, long-context handling, and stateful workflows), so agents can work over large and evolving information.

• Prototype and productionize
  Take ideas from notebook-level prototypes to robust, observable, production-ready systems, including APIs, services, and internal tools.

• Work with foundation models
  Fine-tune, adapt, and post-train large models (e.g., instruction-tuning, RAG, tool-use finetuning, preference optimization) in collaboration with senior ML engineers.

• Experiment and evaluate
  Design experiments, build evaluation harnesses, and analyze metrics to compare different prompts, policies, and agent strategies.

• 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 product, 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.

Required 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.

• Strong programming experience in Python.

• 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).

• Familiarity with at least one modern ML framework such as PyTorch, TensorFlow, or JAX.

• Conceptual understanding of large language models (LLMs) or other foundation models (what they are, common use cases, limitations).

• 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.

• Clear written and verbal communication skills.

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

Preferred Qualifications:

• Projects involving agents, tool use, RAG, or workflow/orchestration frameworks (e.g., LangChain, LlamaIndex, custom planners/executors).

• Exposure to context and memory mechanisms: retrieval/vector stores, long-context handling, or session/state management for LLM applications.

• Experience with foundation model fine-tuning or parameter-efficient methods (e.g., LoRA), or post-training techniques (e.g., instruction-tuning, preference optimization).

• Backend experience: building APIs/services, working with cloud platforms and containerization (e.g., Docker), and basic MLOps (logging, monitoring, experiment tracking).

• Notable ML/AI projects, open-source contributions, or research in ML, NLP, RL, or systems for ML.

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