
Artificial Intelligence Model Developer
2 weeks ago
Key Roles and Responsibilities:
">Develop and deploy machine learning models using Python scripts to enable real-time, batch, event-triggered, and edge deployments.
Utilize AWS Sagemaker for deployment of ML pipelines and ML models using Sagemaker pipelines, Sagemaker MLflow, and Sagemaker Feature Store.
Design and develop APIs using FastAPI, Flask, and Django with at least 3 years of experience in each area.
Assist in model development, training, validation, deployment, and monitoring, as well as ETL processes for ML pipelines using PySpark, Kafka, and AWS EMR Serverless.
Good-to-have skills include containerization using Docker and orchestration using Kubernetes.
">Required Skills and Qualifications:
">- 5+ years of experience in scripting ML workflows in Python.
- 4+ years of experience using AWS Sagemaker.
- A strong background in developing APIs using FastAPI, Flask, and Django.
- Solid understanding of ML lifecycle: model development, training, validation, deployment, and monitoring.
- Experience with ETL processes for ML pipelines using PySpark, Kafka, and AWS EMR Serverless.
The ideal candidate will have a strong foundation in machine learning engineering principles and practices. They will be able to work independently and collaboratively as part of a team, with excellent communication and problem-solving skills.
Our company values innovation, creativity, and continuous learning. We offer opportunities for professional growth and development, as well as a dynamic and supportive work environment.
In this role, you will have the opportunity to work on challenging projects, collaborate with talented professionals, and contribute to the success of our organization.
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