ML (Machine Learning) Engineer

Job Description:

We are seeking Full Stack ML Engineers to support the Hyper Personalization program for our Wealth client, a key initiative aimed at enhancing personalization within financial services. This role requires strong delivery-focused individuals with a deep understanding of the AWS tech stack and financial services personalization.

Responsibilities:

• Integrate AI/ML models with multiple data sources: Ensure seamless data flow in and out of models.
• Fine-tune existing models: Optimize performance and adapt models to evolving requirements.
• Build and maintain data pipelines: Design and implement ETL processes to support model integration.
• Monitor and manage ML models in production: Implement MLOps practices for model monitoring, tracking, and maintenance.
• Collaborate with cross-functional teams: Work closely with data scientists, data engineers, and other stakeholders to deliver robust ML solutions.
• Drive architecture and engineering best practices: Lead efforts to establish and enforce best practices in building the integration framework.

Technical Skills:

• Proficiency in Python and SQL databases: Essential for data manipulation and integration tasks.
• Experience with AWS cloud services: Including but not limited to:
o SageMaker
o Lambda
o Glue
o S3
o IAM
o CodeCommit
o CodePipeline
o Bedrock
• Experience with data pipeline and workflow management tools: Such as Apache Airflow or AWS Step Functions.
• Understanding of ETL techniques, data modeling, and data warehousing concepts: To build efficient data pipelines.
• Familiarity with AI/ML platforms and tools: Including TensorFlow, PyTorch, MLflow, and others.
• Knowledge of MLOps practices: Including model monitoring, data drift detection, and pipeline automation.
• Experience with Docker and AWS ECR: For containerization of ML applications."