Advisory AI Prototyping Engineer

Job Summary

We are Lenovo. We are seeking an AI Prototyping Engineer to join the LATC team. The role involves turning emerging AI ideas into tangible, working demonstrations, operating at the intersection of research, product, and business, and moving fast without breaking trust.

Responsibilities

• Rapid Prototyping Experimentation
• Design, build, and iterate on AI agent prototypes and interactive demos that showcase novel capabilities across Lenovo's product ecosystem (mobile, edge, enterprise).
• Explore cutting-edge agentic frameworks, LLMs, and multimodal models to evaluate their fit for Lenovo's use cases.
• Maintain a "prototype-first" mindset — ship working demos in days, not months.
• Design, implement, and test components for a AI-driven development ecosystem, including model-powered tools and platform services that enhance the software delivery lifecycle.
• Develop intelligent agents and automated workflows that streamline software build, testing, deployment, and operational tasks across CI/CD pipelines.
• Define, evaluate, and unify APIs, namespaces, and integration patterns across contributions from multiple developers, ensuring consistent interface design and long-term architectural coherence within the platform.
• Develop/integrate platforms for AI models and agent behaviors assessment for performance, accuracy, robustness, and fairness, and provide actionable insights to improve reliability and developer experience.

Technical Feasibility Research

• Conduct hands-on technical investigations of new AI models, tools, and frameworks, and produce clear feasibility reports summarizing findings, trade-offs, and recommendations.
• Work closely with the LATC RD team to translate research outputs into prototype-ready implementations.
• Identify promising technologies early and advocate for their adoption or de-risking.

Cross-Team Collaboration

• Partner with Business Group (BG) product teams to understand real user needs and translate them into prototype specifications.
• Act as a technical bridge between LATC technology pillars and business stakeholders, ensuring prototypes address genuine product priorities.
• Work closely with mentors and team members to document findings, share insights, and contribute to non-coding project deliverables.

Stakeholder Communication

• Present prototype demos and technical findings to internal stakeholders, including department leaders and BG partners, in a clear and compelling way.
• Create supporting materials (slide decks, short videos, written summaries) that communicate the "so what" of each prototype.
• Gather feedback from demos and rapidly incorporate it into the next iteration.

Engineering Craft

• Write clean, well-documented prototype code that can be handed off to production engineers when a concept is validated.
• Contribute to shared tooling, reusable components, and internal knowledge bases that accelerate future prototyping cycles.

Required Qualifications

• 3–5 years of software engineering experience, with at least 1 year focused on ML/AI systems or LLM-based applications.
• BS/MS in Computer Science, AI/ML, or related field; equivalent practical experience considered.
• Strong Python skills, including experience with async patterns and working with AI/ML libraries.
• Hands-on experience with agentic frameworks such as LangChain, LangGraph, LlamaIndex, or AutoGen.
• Demonstrated ability to build functional AI prototypes or demos quickly, ideally with examples to show.
• Comfortable working in ambiguous, fast-moving environments with shifting priorities.
• Strong communication skills — able to explain technical concepts to both engineers and non-technical stakeholders.
• Experience working across teams and gathering requirements from product or business partners.
• A portfolio of demos, side projects, blog posts, or open-source contributions showcasing AI work.

Preferred Qualifications

• Experience with multimodal models (vision, speech, or on-device AI).
• Background in edge or mobile AI deployment (latency-constrained environments).
• Familiarity with MCP (Model Context Protocol) or similar agent communication protocols.
• Experience with MLOps tools or experiment tracking frameworks (MLflow, WB, etc.).
• Comfort with presentation tools and an eye for communicating ideas visually.