Advisory AI Prototyping Engineer

📍 Job Overview

Job Title: Advisory AI Prototyping Engineer

Company: Lenovo

Location: Morrisville, North Carolina, United States

Job Type: Full-time

Category: Artificial Intelligence / Software Engineering

Date Posted: October 02, 2026

Experience Level: 3-5 years (with at least 1 year in ML/AI systems or LLM applications)

Remote Status: On-site

🚀 Role Summary

• Drive innovation by transforming emerging AI concepts into tangible, working prototypes and interactive demonstrations within Lenovo's product ecosystem.

• Operate at the critical intersection of AI research, product development, and business strategy to validate technical feasibility and showcase novel capabilities.

• Develop and iterate on AI agent prototypes, leveraging cutting-edge LLMs, agentic frameworks, and multimodal models to solve real-world problems.

• Act as a technical bridge, translating complex AI research outputs into accessible prototypes for both engineering teams and business stakeholders.

📝 Enhancement Note: This role is positioned within the LATC (Lenovo Artificial Intelligence Technology Center) team, indicating a focus on advanced AI research and application within Lenovo's broader technology strategy. The emphasis on "rapid prototyping" and "shipping working demos in days, not months" suggests a dynamic, agile environment where quick iteration and demonstrable results are paramount. This is not a pure research role, but one that requires bridging research with practical application and product-readiness.

📈 Primary Responsibilities

• Design, build, and iterate on AI agent prototypes and interactive demos to showcase novel capabilities across Lenovo's diverse product ecosystem, including mobile, edge, and enterprise devices.

• Explore and evaluate cutting-edge agentic frameworks (e.g., LangChain, LangGraph, LlamaIndex, AutoGen), LLMs, and multimodal models for their suitability and potential impact on Lenovo's use cases.

• Maintain a "prototype-first" mindset, prioritizing rapid development and iteration to deliver working demos quickly and efficiently.

• Design, implement, and test components for an AI-driven development ecosystem, including model-powered tools and platform services that enhance the software delivery lifecycle.

• Develop intelligent agents and automated workflows to streamline software build, testing, deployment, and operational tasks within CI/CD pipelines.

• Define, evaluate, and unify APIs, namespaces, and integration patterns across multiple developer contributions, ensuring architectural coherence and consistent interface design within the platform.

• Develop and integrate platforms for assessing AI models and agent behaviors for performance, accuracy, robustness, and fairness, providing actionable insights for improvement.

• Conduct hands-on technical investigations of new AI models, tools, and frameworks, producing clear feasibility reports with summarized findings, trade-offs, and recommendations.

• Work closely with the LATC R&D team to translate research outputs into prototype-ready implementations and identify promising technologies for early adoption.

• Partner with Business Group (BG) product teams to understand user needs and translate them into prototype specifications, acting as a technical bridge to ensure prototypes address genuine product priorities.

• Present prototype demos and technical findings to internal stakeholders, including department leaders and BG partners, in a clear and compelling manner.

• Create supporting materials such as slide decks, short videos, and written summaries to effectively communicate the impact and "so what" of each prototype.

• Gather feedback from demos and rapidly incorporate it into subsequent iterations to drive continuous improvement.

• Write clean, well-documented prototype code that is readily transferable to production engineers upon successful validation of a concept.

• Contribute to shared tooling, reusable components, and internal knowledge bases to accelerate future prototyping cycles and enhance team efficiency.

📝 Enhancement Note: The responsibilities highlight a strong emphasis on practical application, rapid iteration, and cross-functional collaboration. The inclusion of CI/CD pipeline integration, API standardization, and MLOps tooling indicates a need for a pragmatic approach to AI prototyping that considers eventual production readiness and maintainability. The role requires not just technical skill but also strong communication and stakeholder management abilities.

🎓 Skills & Qualifications

Education:

• Bachelor's degree in Computer Science, AI/ML, or a related technical field.

• Master's degree in Computer Science, AI/ML, or a related technical field is preferred.

• Equivalent practical experience will be considered in lieu of formal degrees. Experience:

• 3–5 years of professional software engineering experience.

• Minimum of 1 year of dedicated experience focused on Machine Learning (ML) / Artificial Intelligence (AI) systems or Large Language Model (LLM)-based applications. Required Skills:

• Python Proficiency: Strong command of Python programming, including experience with asynchronous patterns and leveraging AI/ML libraries.

• Agentic Frameworks: Hands-on experience with popular agentic frameworks such as LangChain, LangGraph, LlamaIndex, or AutoGen.

• AI Prototyping: Demonstrated ability to rapidly build functional AI prototypes or interactive demos, with a portfolio showcasing past work.

• Ambiguity Navigation: Comfort and effectiveness in ambiguous, fast-moving environments with evolving priorities.

• Communication Skills: Excellent verbal and written communication skills, with the ability to clearly articulate complex technical concepts to both engineering and non-technical audiences.

• Cross-Functional Collaboration: Proven experience working across different teams and gathering requirements from product or business partners.

• Portfolio: A portfolio of demos, side projects, blog posts, or open-source contributions that effectively showcase AI work.

Preferred Skills:

• Multimodal Models: Experience with multimodal models, including those involving vision, speech, or on-device AI processing.

• Edge/Mobile AI: Background in deploying AI models in edge or mobile environments, particularly in latency-constrained settings.

• Agent Communication Protocols: Familiarity with protocols like MCP (Model Context Protocol) or similar agent communication standards.

• MLOps Tools: Experience with MLOps tools or experiment tracking frameworks such as MLflow, Weights & Biases (W&B).

• Visual Communication: Proficiency with presentation tools and a strong ability to communicate ideas visually.

📝 Enhancement Note: The experience requirement clearly delineates a need for both foundational software engineering skills and specialized AI/ML experience, particularly with LLMs and agentic frameworks. The emphasis on a "portfolio" suggests that practical, demonstrable skills are highly valued over theoretical knowledge alone. The preferred qualifications point towards future-oriented AI development areas like multimodal and edge AI.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrable AI Prototypes: Showcase a collection of functional AI prototypes, interactive demos, or proof-of-concepts that highlight your ability to bring AI ideas to life.

• Technical Feasibility Examples: Include projects that illustrate your process for evaluating the technical feasibility of new AI models or frameworks, detailing any trade-offs or recommendations made.

• Agentic Framework Implementations: Present examples of using agentic frameworks (LangChain, AutoGen, etc.) to build intelligent agents or automated workflows, detailing the problem solved and the architecture.

• Code Repository Links: Provide links to GitHub repositories or similar platforms where your prototype code is hosted, ensuring it is clean, well-documented, and representative of your engineering craft.

• Performance Metrics & Insights: If applicable, include data on prototype performance, accuracy, or efficiency, along with insights derived from testing and evaluation.

Process Documentation:

• Workflow Design & Optimization: Demonstrate experience in designing and optimizing workflows for AI development, data processing, or automated tasks within CI/CD pipelines.

• API & Integration Patterns: Showcase examples of defining, integrating, or unifying APIs and integration patterns to ensure architectural coherence and efficient system interaction.

• Model/Agent Assessment: Illustrate your approach to defining, evaluating, and testing AI models or agent behaviors for performance, robustness, and fairness, including how actionable insights were generated.

• Research Translation: Provide examples of how you have translated research outputs or identified promising technologies into practical, prototype-ready implementations.

📝 Enhancement Note: For an AI Prototyping Engineer role, the portfolio is critical. It serves as tangible proof of capability. Candidates should curate examples that directly align with the responsibilities, focusing on rapid development, agentic systems, and the ability to translate complex ideas into working code. Demonstrating an understanding of how prototypes connect to broader development ecosystems (CI/CD, APIs) will be highly advantageous.

💵 Compensation & Benefits

Salary Range:

• Based on industry benchmarks for an AI Prototyping Engineer with 3-5 years of experience in the Morrisville, North Carolina area, the estimated salary range is $110,000 - $150,000 USD per year. This range accounts for the specialized skills in AI/ML, LLMs, and rapid prototyping, as well as the cost of living in the Research Triangle Park region. Benefits:

• Comprehensive Health Coverage: Medical, dental, and vision insurance plans.

• Retirement Savings Plan: 401(k) with potential company match.

• Paid Time Off: Generous vacation, sick leave, and paid holidays.

• Professional Development: Opportunities for training, conferences, and certifications in AI/ML.

• Employee Discounts: Discounts on Lenovo products and services.

• Innovation Culture: Access to cutting