iSoftTek Solutions Inc

Machine Learning Engineer

iSoftTek Solutions Inc

contract

Posted on:

Origin:  • 🇺🇸 United States • North Carolina, Pennsylvania

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Job Level

SeniorLead

Tech Stack

AirflowApacheAWSCloudDockerETLPythonPyTorchSQLTensorflow

About the role

  • ML Engineer
  • Location: Charlotte, NC or Malvern, PA (hybrid – 3 days/week from office)
  • Duration: 06 months
  • yrs of exp:10
  • Overview: 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: SageMaker, Lambda, Glue, S3, IAM, CodeCommit, CodePipeline, 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.

Requirements

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