Skip to main content
Loading
loadingbar
Loading, Please wait..!!

Onsite AI Solutions Engineer — ML, MLOps & Cloud

  • Job type Posted on: Jul 14, 2026
  • Experience level CyberCoders
  • Employment type Raleigh, North Carolina
  • Employment type Onsite
  • Salary Full-time

Point Apply Here APPLY LATER

Curious about compensation?

Explore the historical salary trends, average pay, and estimated compensation for Onsite AI Solutions Engineer — ML, MLOps & Cloud roles in North Carolina.

View Salary Guide →

Job Title :

Onsite AI Solutions Engineer — ML, MLOps & Cloud

Job Type :

Full-time

Job Location :

Raleigh North Carolina United States

Remote :

No

Jobcon Logo Job Description :

CyberCoders is seeking an AI Solutions Engineer to design and deliver AI-driven solutions across data science, ML, and software engineering. You will build scalable AI applications addressing business challenges and collaborate with cross-functional teams to translate requirements into actionable models. Responsibilities include developing ML models in Python (PyTorch, TensorFlow), deploying on cloud platforms, and applying MLOps practices for production readiness. #J-18808-Ljbffr

Jobcon Logo Position Details

Posted:

Jul 14, 2026

Reference Number:

14660_5F516FF51BFCB4031AB74CFF35B05567

Employment:

Full-time

Salary:

Not Available

City:

Raleigh

Job Origin:

APPCAST_CPC

Share this job:

  • linkedin

Jobcon Logo
A job sourcing event
In Dallas Fort Worth
Aug 19, 2017 9am-6pm
All job seekers welcome!

Onsite AI Solutions Engineer — ML, MLOps & Cloud    Apply

Click on the below icons to share this job to Linkedin, Twitter!

CyberCoders is seeking an AI Solutions Engineer to design and deliver AI-driven solutions across data science, ML, and software engineering. You will build scalable AI applications addressing business challenges and collaborate with cross-functional teams to translate requirements into actionable models. Responsibilities include developing ML models in Python (PyTorch, TensorFlow), deploying on cloud platforms, and applying MLOps practices for production readiness. #J-18808-Ljbffr

Loading
Please wait..!!