AWS SageMaker Consultant

Job Title: SageMaker Consultant / ML Engineer

Designation: Tech Lead Consultant / ML Engineer

Location: Charlotte, North Carolina, US

Role Summary

The AWS SageMaker Consultant is responsible for designing, developing, deploying, and operating machine learning solutions using Amazon SageMaker. The role covers the end‑to‑end ML lifecycle, including data preparation, model training, validation, deployment, and MLOps, ensuring scalable, secure, and production‑ready AI solutions.

Responsibilities

· Configure and manage Amazon SageMaker Studio / Notebooks / Workbench

· · SageMaker Platform Setup & Administration

· · Set up and maintain SageMaker environments

· · Manage IAM roles, permissions, and secure access controls

· · Prepare training, validation, and test datasets

· · Use SageMaker Processing Jobs for scalable data preprocessing

· · Integrating SageMaker with Amazon S3, Data Lakes, and enterprise data sources

· · Develop ML models using SageMaker built‑in algorithms or custom training scripts

(scikit‑learn, XGBoost, TensorFlow, PyTorch)

· · Configure training jobs, distributed training, and hyperparameter tuning

Track experiments, metrics, and artifacts

· · Ensure model reproducibility and explainabilityModel Validation & Testing

· · Perform functional validation, regression testing, and performance benchmarking

· · Evaluate models using metrics such as accuracy, precision, recall, RMSE, MAE

· · Implement automated model validation pipelines

· · Deploy models as real‑time inference endpoints

· · Build and maintain MLOps pipelines for model training and promotion

· · Manage model versioning, registries, and artifacts

· · Integrate SageMaker with Data Warehouses, Snowflake, BI platforms, and APIs

· · Support event‑driven and batch integrations

· · Collaborate with Data Engineering, Cloud, and Security teams

· · Support audit, compliance, and documentation requirements

Technical Skills

· · SageMaker (Studio, Training Jobs, Endpoints, Batch Transform)

· · Python and ML libraries (scikit‑learn, TensorFlow, PyTorch, XGBoost)

· · Data preprocessing, feature engineering, and model evaluation