Description

You will build and integrate ML solutions for hyper-personalization in 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 evolving requirements.
• Design and implement ETL processes and data pipelines to support model integration.
• Implement MLOps practices for monitoring, tracking, and maintaining models in production.
• Drive engineering best practices for building integration frameworks.

Required Skills

• Proficiency in Python and SQL databases.
• Experience with AWS services including Lambda, Glue, CodeCommit, S3, IAM, CodePipeline, SageMaker, 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 practices, including model monitoring and data drift detection.
• Experience with Docker and AWS ECR for containerization.
• Any graduate degree.

Key Skills

Education

Any graduate