AI/ML Engineer

The AI/ML Engineer is a core member of the AI/AWS Team responsible for designing and deploying machine learning models and data pipelines on the cloud/AWS. This role focuses on the technical setup, configuration, and management of large-scale data ingestion and transformation processes on the cloud/AWS, leveraging automation to accelerate the data science and ML operationalization (MLOps) journey., * Data Transformation and ML Pipeline Management

• Configure and manage the cloud/AWS services (e.g., AWS Glue, Sagemaker Data Wrangler) to set up data connectors and execute large-scale data transformation jobs.

• Select and execute AI/ML capabilities such as feature engineering, data quality checks, model training, performance analysis, and model deployment pipelines (MLOps).

• Review and assess model training job outputs, including feature importance reports, data drift metrics, and model performance baselines, to inform deployment decisions.

• Platform and Infrastructure

• Set up and secure the cloud/AWSaccounts, S3 buckets, and configure necessary IAM permissions to enable secure data transfer and access for ML workflows.

• Provision and manage target cloud infrastructure for ML model serving and data processing using Infrastructure as Code (IaC) templates (AWS CloudFormation, the cloud/AWS Cloud Development Kit (CDK), or Terraform).

• Manage CI/CD/CD (or MLOps) pipelines to facilitate the deployment and continuous integration of models and microservices.

• Model and Data Handling

• Organize and manage large datasets and required code artifacts-including training data, feature stores, Python scripts, and Jupyter notebooks-into secure data repositories (e.g., S3).

• Develop and review production-grade model code and associated scripts (e.g., for inference) to ensure performance and maintainability, optionally enabling monitoring tools for model quality and drift detection.

• Model Testing and Validation

• Generate test artifacts, including model validation metrics and test automation scripts, to support functional and performance testing of deployed ML models.

Do you have experience in Tooling?, * Experience configuring and managing the cloud/AWSservices, specifically Amazon S3 and IAM permissions, and ML services like Amazon SageMaker, within an enterprise environment.

• Technical understanding of machine learning principles, model lifecycle management, and MLOps practices.

• Proficiency with Infrastructure as Code (IaC) tooling, such as AWS CloudFormation, AWS CDK, or Terraform.

• Knowledge of cloud-native development and deployment practices, including microservices, CI/CD, and AWS compute services (ECS, EKS, Lambda, Fargate).

• Familiarity with data transformation and processing methodologies (e.g., Spark, AWS Glue, EMR) and the phases of the ML lifecycle (Data Prep, Training, Tuning, Deployment, Monitoring).