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.

Preferred Skills

  • Experience in the financial services domain.

Key Skills
Education

Any graduate

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