

Smart IT Frame LLC
AWS Sagemaker Developer
β - Featured Role | Apply direct with Data Freelance Hub
This role is for an AWS Sagemaker Developer in Charlotte, North Carolina, on a contract basis. Key skills include AWS, SageMaker, and deep learning. Experience in building and optimizing ML workflows is required. Pay rate is unspecified.
π - Country
United States
π± - Currency
$ USD
-
π° - Day rate
Unknown
-
ποΈ - Date
November 12, 2025
π - Duration
Unknown
-
ποΈ - Location
On-site
-
π - Contract
Unknown
-
π - Security
Unknown
-
π - Location detailed
Charlotte, NC
-
π§ - Skills detailed
#Batch #SageMaker #AWS SageMaker #AWS (Amazon Web Services) #Deep Learning #ML (Machine Learning)
Role description
Job Title: AWS Sagemaker Developer
Location: Charlotte, North Carolina, United States
Employment Type: Contract
At Smart IT Frame, we connect top talent with leading organizations across the USA. With over a decade of staffing excellence, we specialize in IT, healthcare, and professional roles, empowering both clients and candidates to grow together.
Key Responsibilities:
β’ Design, build, and optimize end-to-end ML workflows using AWS SageMaker.
β’ Develop and deploy machine learning models (supervised, unsupervised, and deep learning).
β’ Implement feature engineering, model training, evaluation, and hyperparameter tuning.
β’ Use SageMaker Studio, SageMaker Pipelines, and SageMaker Experiments to manage ML lifecycle.
β’ Package and deploy models as real-time endpoints or batch inference jobs.
Must Have Skills:
AWS
Sagemaker
Apply today or share profiles at Hariharan@smartitframe.com
Job Title: AWS Sagemaker Developer
Location: Charlotte, North Carolina, United States
Employment Type: Contract
At Smart IT Frame, we connect top talent with leading organizations across the USA. With over a decade of staffing excellence, we specialize in IT, healthcare, and professional roles, empowering both clients and candidates to grow together.
Key Responsibilities:
β’ Design, build, and optimize end-to-end ML workflows using AWS SageMaker.
β’ Develop and deploy machine learning models (supervised, unsupervised, and deep learning).
β’ Implement feature engineering, model training, evaluation, and hyperparameter tuning.
β’ Use SageMaker Studio, SageMaker Pipelines, and SageMaker Experiments to manage ML lifecycle.
β’ Package and deploy models as real-time endpoints or batch inference jobs.
Must Have Skills:
AWS
Sagemaker
Apply today or share profiles at Hariharan@smartitframe.com





