AI Cluster Infrastructure Architect & Performance Engineer

Lenovo; Morrisville (NC); On-site; USD 120,000 - 180,000; Full time

Job summary

Lenovo in Morrisville, North Carolina is seeking an AI infrastructure engineer to design and validate cluster infrastructure management solutions, and to develop deployment methodologies, software frameworks and tools for end-to-end performance evaluation and tuning.

You will design features that improve AI cluster performance, collaborate with cross-functional teams on customer engagements, and apply knowledge of ML/DL concepts, PyTorch, and related tooling to production-ready environments.

Qualifications

• BS/MS in CS/CE/EE or related field.
• 2+ years in datacenter hardware/software design/implementation.
• Strong programming in C/C++, Python, Go, shell.
• Knowledge of ML/DL concepts.
• Experience with ML frameworks: PyTorch, LangChain, LangGraph, Autogen.
• Excellent communication and leadership.
• Fast learner with innovative mindset.

Responsibilities

• Design and validate AI cluster infrastructure management solutions.
• Develop deployment methodologies, software frameworks and tools for AI cluster deployment and end-to-end performance evaluation and tuning.
• Design and develop hardware and software product features to improve AI cluster solutions in performance, manageability, and serviceability.
• Collaborate with cross-functional teams and contribute to business projects and customer engagements.

Skills

C/C++

Python

Go

Shell scripting

Machine Learning concepts

Communication

Leadership

Learning fast

Innovative mindset

Education

BS in Computer Science/Engineering

MS in Computer Science/Engineering

Tools

PyTorch

LangChain

LangGraph

Autogen

Job description

Lenovo in Morrisville, North Carolina is seeking an AI infrastructure engineer to design and validate cluster infrastructure management solutions, and to develop deployment methodologies, software frameworks and tools for end-to-end performance evaluation and tuning.

You will design features that improve AI cluster performance, collaborate with cross-functional teams on customer engagements, and apply knowledge of ML/DL concepts, PyTorch, and related tooling to production-ready environments.