About this role
The Advisory AI Prototyping Engineer joins Lenovo’s LATC team to turn emerging AI ideas into working demonstrations. The role combines hands-on development, technical feasibility research, and communication with engineering, product, and business stakeholders.
Responsibilities
- Rapidly design and iterate on AI agent prototypes and interactive demos for mobile, edge, and enterprise use cases. Assess agentic frameworks, large language models, and multimodal models for potential applications.
- Build and test model-powered tools and platform services for an AI-driven software development ecosystem. Develop agents and workflows that support build, testing, deployment, and operations across CI/CD pipelines.
- Define and align APIs, namespaces, and integration patterns across developers. Integrate platforms that assess AI models and agent behaviors for performance, accuracy, robustness, and fairness, and use findings to improve reliability and developer experience.
- Investigate emerging models, tools, and frameworks; document feasibility, trade-offs, and recommendations; and translate LATC R&D work into prototypes. Gather product requirements, present demos and findings, prepare supporting materials, and incorporate stakeholder feedback.
- Write documented prototype code suitable for handoff to production engineers, and contribute reusable components, shared tooling, and project documentation.
Required qualifications
3–5 years of software engineering experience, including at least 1 year focused on ML/AI systems or LLM-based applications. A BS/MS in Computer Science, AI/ML, or a related field is required, with equivalent practical experience considered. Candidates need strong Python skills, including async patterns and AI/ML libraries; hands-on experience with agentic frameworks such as LangChain, LangGraph, LlamaIndex, or AutoGen; and the ability to build functional AI prototypes quickly. The role also requires communication, cross-team requirements gathering, comfort with shifting priorities, and a portfolio demonstrating AI work.
Preferred qualifications
Experience with multimodal models, edge or mobile AI deployment, Model Context Protocol or similar protocols, MLOps or experiment-tracking tools such as MLflow or W&B, and visual presentations.