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ML Engineer

4 months ago

Charlotte, United States iSoftTek Solutions Inc Full time
Job DescriptionJob Description

ML Engineer

Location: Charlotte, NC or Malvern, PA (hybrid – 3 days/week from office)

Duration: 06 months

yrs of exp:10

Job Description:

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:

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.