GENERAL INFORMATION

Req #
WD00087368
Career area:
Hardware Engineering
Country/Region:
United States of America
State:
North Carolina
City:
Morrisville
Date:
Wednesday, August 27, 2025
Working time:
Full-time

Additional Locations:

• United States of America - North Carolina - Morrisville

POSITION DESCRIPTION

At Lenovo, we Never Stand Still. Every day, every employee at Lenovo is focused on moving forward, rejecting traditional limits, and always seeking a better way.
We’re looking for a Cloud AI Architect within the Global Innovation Center (GIC) Group at Lenovo. In this role As an AI and Large Language Model Architect, you will play a pivotal role in designing and implementing the technology architecture for advanced AI (including Large Language Model (LLM)) platform systems and solutions.
This role demands a skilled professional capable of designing and implementing advanced agentic AI systems that leverage both the Model Context Protocol (MCP) for contextual grounding and Agent-to-Agent (A2A) communication for seamless collaboration and task delegation between agents.
You will translate business roadmap into technical requirements and architecture. Your contributions will be instrumental in shaping cutting-edge architectures, frameworks, and methodologies, pushing the boundaries of natural language processing (NLP) and machine learning.
You will architect sophisticated large language models (LLMs) with the remarkable ability to process and generate natural language. Additionally, you will have the opportunity to design neural network parameters, leveraging extensive amounts of unlabeled text data, to further enhance the model’s capabilities.

BASIC QUALIFICATIONS

• BA/BS degree in Computer Science or related software engineering field, or equivalent experience.
• 6+ years of experience in designing & deploying AI / ML solutions using at least one cloud vendor
• 1+ year of experience in the LLM and Generative AI space
• 1+ year of experience architecting and operationalizing LLM driven application architecture patterns

PREFERRED QUALIFICATIONS

• Deep understanding of AI/ML concepts, including LLMs, transformers, and prompt engineering.
• Experience with agentic AI frameworks such as LangChain, AutoGPT, Agentforce, LangGraph, or similar.
• Familiarity with the Model Context Protocol (MCP) standard and its role in providing context to AI agents.
• Experience with Agent-to-Agent (A2A) communication protocols and frameworks, enabling multi-agent collaboration and task delegation.
• 2+ Experience with specific AI/ML frameworks and tools (e.g., TensorFlow, PyTorch, scikit-learn, MLflow)
• Demonstrable experience implementing and maintaining globally distributed, highly redundant, scalable cloud-hosted solutions
• Ability to demonstrate knowledge of a container technology such as Docker
• Proficient technical knowledge of current tools and best practices at scale
• Demonstrable experience working with distributed teams & 3rd-party vendors
• Experience with monitoring and logging cloud services and infrastructure
• Experience using code management tooling such as Git/SVN/CVS
• Significant experience working with Linux servers and command lines
• Strong written and verbal communication skills
  We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, religion, sexual orientation, gender identity, national origin, status as a veteran, and basis of disability or any federal, state, or local protected class.

Additional Locations:

• United States of America - North Carolina - Morrisville
• United States of America
• United States of America - North Carolina
• United States of America - North Carolina - Morrisville

• Produce high quality architecture specifically on AI, including LLMs, Inference Engineering and Prompt Engineering and design specifications.
• Experience finetuning an Opensource LLM
• Architect and design end to end Generative AI products, applications and solutions for specific business needs and provide implementation guidance during delivery.
• Designing and implementing autonomous AI agents capable of reasoning, planning, acting, and adapting to achieve complex objectives.
• Integrating Large Language Models (LLMs) with memory, tool-use, and multi-step planning architectures, leveraging their natural language understanding and generation capabilities as the agent’s “brain”.
• Utilizing MCP servers or similar mechanisms to enable AI agents to interact with external enterprise applications, databases, and APIs.
• Designing and implementing agentic workflows where multiple agents communicate and collaborate using the A2A protocol to achieve shared goals.
• Analyze and evaluate the performance of Gen AI systems and provide design recommendations.
• Research, design, and implement machine learning models and algorithms, with a focus on LLM and Deep Learning techniques.
• Collaborate with cross-functional teams to identify business problems and opportunities where machine learning solutions can add value.
• Develop and deploy scalable and efficient machine learning pipelines for processing and analyzing large volumes of structured and unstructured data.
• Perform data preprocessing, feature engineering, and model training/validation using state-of-the-art machine learning frameworks and libraries
• Evaluate model performance and interpret results to derive actionable insights and recommendations.
• Estimate cost of using LLMs in different forms for business use cases and the viability, develop cost models for different usage patterns.
• Design and prototype reusable components for LLM based solution patterns.
• Architect components of an LLM solution to address Responsible AI & Security
• Collaborate seamlessly with diverse, cross-functional teams to accurately identify and prioritize requirements, ensuring that the language model meets the needs and expectations of various stakeholders.
• Create and maintain comprehensive technical documentation that comprehensibly captures the intricate details of the language model, facilitating seamless understanding, efficient troubleshooting, and future development.
• Harness the power of transformer architecture, a cutting-edge deep learning model widely employed in natural language processing and computer vision, to optimize the language model’s performance and efficiency.