Associate engineer serp_jobs.h1.location_city

Description and Requirements

Lenovo is seeking a highly motivated AIEngineer to contribute to the design,development, and exploration of our next-generation AI systems.As an ML & Model Training AI Engineer, you 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 planners, executors, evaluators).

Design context and memory management

Help design and implement strategies for managing context and “memory” across interactions 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 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.

Qualifications

Bachelor’s degree in Computer Science, Computer Engineering, or a closely related technical field 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 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 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.

This position is NOT ELIGIBLE FOR VISA SPONSORSHIP, including Optional Practical Training (OPT) or Curricular Practical Training (CPT). All applicants must be currently authorized to work in the United States for any employer.

Bonus Points

Projects involving agents , tool use , RAG , or workflow / orchestration frameworks 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 LoRA), or post-training techniques instruction-tuning, preference optimization).

Backend experience : building APIs / services, working with cloud platforms and containerization 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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