ML Engineer

Ampcus - Malvern, PA

Full-time

Malvern, PA

Professional, Scientific, and Technical Services

About the position

We are seeking a highly skilled ML Engineer to join our team in Malvern, PA, focusing on the Hyper Personalization program for our Wealth client. This initiative is crucial for enhancing personalization within financial services, and we are looking for delivery-focused individuals who possess a deep understanding of the AWS tech stack and financial services personalization. The ideal candidate will be responsible for integrating AI and client models with multiple data sources, ensuring a seamless flow of data in and out of these models. This role requires a proactive approach to fine-tuning existing models to optimize their performance and adapt them to evolving requirements. In addition to model integration, the ML Engineer will be tasked with building and maintaining data pipelines. This includes designing and implementing ETL processes that support model integration, which is vital for the success of our personalization efforts. The candidate will also monitor and manage client models in production, implementing MLOps practices for effective model monitoring, tracking, and maintenance. Collaboration is key in this role, as the ML Engineer will work closely with cross-functional teams, including data scientists, data engineers, and other stakeholders, to deliver robust client solutions. Furthermore, the ML Engineer will drive architecture and engineering best practices, leading efforts to establish and enforce best practices in building the integration framework. This position is not only about technical skills but also about leadership and collaboration, making it essential for the candidate to have strong communication skills and the ability to work in a team-oriented environment.

Responsibilities

• Integrate AI/Client models with multiple data sources to ensure seamless data flow in and out of models.
• Fine-tune existing models to optimize performance and adapt to evolving requirements.
• Build and maintain data pipelines by designing and implementing ETL processes to support model integration.
• Monitor and manage Client models in production by implementing MLOps practices for model monitoring, tracking, and maintenance.
• Collaborate with cross-functional teams, including data scientists, data engineers, and other stakeholders, to deliver robust Client solutions.
• Drive architecture and engineering best practices by leading efforts to establish and enforce best practices in building the integration framework.

Requirements

• Proficiency in Python and SQL databases for data manipulation and integration tasks.
• Experience with AWS cloud services, including SageMaker, Lambda, Glue, S3, IAM, CodeCommit, CodePipeline, and 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/Client 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 Client applications.