Junior Data & AI/ML Engineer - ML Pipelines

Lenovo; Morrisville (NC); On-site; USD 110,000 - 150,000; Full time

Job summary

Lenovo’s Advanced Innovation Center in Morrisville, NC, is seeking a Data and AI-ML Engineer to join the team building data pipelines, managing large datasets, and developing machine learning models for Lenovo products and customers.

The role suits those passionate about data engineering, ML, AI and cloud tech, with hands-on experience in Python, SQL, ETL/ELT, and modern data platforms; collaboration across global teams is key.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, IT, or related field.
• 2+ years of experience in data engineering, data science, or ML.
• Strong Python and SQL skills.
• Experience with ETL/ELT, data pipelines, and data lakes.
• Experience with PyTorch, TensorFlow, Scikit-learn, or Pandas.
• Familiarity with Spark, Databricks, or dbt.

Responsibilities

• Build and maintain data models, pipelines, and data products.
• Work with data lakes and cloud-based data platforms.
• Support data governance, quality, metadata, and access controls.
• Build, test, and improve machine learning and AI models.
• Analyze large datasets using Python and SQL.
• Work with global engineering and data teams to develop scalable solutions.

Skills

Python

SQL

ETL/ELT

Data pipelines

Data lakes

Machine learning

PyTorch

TensorFlow

Scikit-learn

Pandas

Spark

Databricks

dbt

Education

Bachelor’s degree in Computer Science/Engineering/IT

Master’s degree (preferred)

Tools

PyTorch

TensorFlow

Scikit-learn

Pandas

Spark

Databricks

dbt

AWS

Power BI

Parquet

Delta Lake

Job description

Lenovo’s Advanced Innovation Center in Morrisville, NC, is seeking a Data and AI-ML Engineer to join the team building data pipelines, managing large datasets, and developing machine learning models for Lenovo products and customers.

The role suits those passionate about data engineering, ML, AI and cloud tech, with hands-on experience in Python, SQL, ETL/ELT, and modern data platforms; collaboration across global teams is key.