Senior Associate, AI Engineer
PwC
CloudDockerKubernetesMicroservices
Machine Learning Engineer
iSoftTek Solutions Inc
contract
Posted on:
Location: North Carolina, Pennsylvania • 🇺🇸 United States
Visit company websiteJob 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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