Description

You will build and maintain the integration framework for a hyper-personalization program within financial services.

Responsibilities

• Integrate AI/ML models with multiple data sources to ensure seamless data flow.
• Fine-tune existing models to optimize performance and adapt to new requirements.
• Design and implement ETL processes and data pipelines to support model integration.
• Monitor and manage ML models in production using MLOps practices.
• Drive engineering best practices for the integration framework.

Required Skills

• Proficiency in Python and SQL databases.
• Extensive experience with AWS services including SageMaker, Lambda, Glue, S3, IAM, CodeCommit, CodePipeline, and Bedrock.
• Experience with data pipeline and workflow management using Apache Airflow or AWS Step Functions.
• Knowledge of ETL techniques, data modeling, and data warehousing.
• Familiarity with AI/ML platforms such as TensorFlow, PyTorch, and MLflow.
• Understanding of MLOps, including model monitoring, data drift detection, and pipeline automation.
• Experience with Docker and AWS ECR for containerization.
• Any graduate degree.

Key Skills

Education

Any graduate