Overview
Hybrid
Depends on Experience
Contract - W2
Skills
AWS
AWS Sagemaker
CI/CD
Jenkins
Docker
Kubernetes
SageMaker Pipelines
MLflow
Kubeflow
Job Details
Role: AWS Sagemaker
Location: Atlanta, GA/ Charlotte NC (3days Hybrid Initially then onsite)(Only locals)
Client: Cognizant/Truist
3-5yrs relevant should be fine
Note:
- Must be willing to work on our W2
Experience:
- 10+ years of experience in data science, machine learning, or cloud-based AI solution development.
- 3+ years of hands-on experience with AWS SageMaker.
- Proven experience deploying ML models in production at scale in enterprise environments (preferably in financial services or technology sectors).
Technical Skills:
- AWS Services: SageMaker, S3, Lambda, Glue, Redshift, ECS/EKS, CloudWatch, Step Functions, IAM, CodePipeline.
- Programming: Python (required), SQL, Bash; familiarity with Java or R is a plus.
- Frameworks: TensorFlow, PyTorch, Scikit-learn, XGBoost, LightGBM.
- Data Tools: Pandas, Spark, Airflow, AWS Data Wrangler.
- MLOps: SageMaker Pipelines, MLflow, Kubeflow, or similar.
- Version Control: Git, GitHub, Bitbucket.
- Containerization: Docker, Kubernetes (EKS preferred).
- Strong understanding of model lifecycle management, monitoring, and retraining strategies
Need Word Format Resume, Below details, Work Authorization Copy (If H1B / EAD).
Candidate Profile:
First Name (as per passport):
Last Name (as per passport):
Phone number:
Email:
LinkedIn:
US Work Authorization:
Current Location:
Relocation:
Availability:
Expected lowest All Inc Hourly Rate on W2:
Hybrid work ( 3 days weekly) initially and then onsite (Yes/ No):
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.