eTeam
JD:
Role Name: Full Stack AI AI_AWS_Engineer
Location: Malvern, PA Onsite
Onsite
JOB DESCRIPTION: Full Stack Data Scientist to develop, deploy, and manage machine learning (ML) and Generative Al (GenAl) and bedrock agents and models across a range of applications, with a focus on integrating AWS solutions. This role involves designing and optimizing predictive models, creating GenAl-driven applications leveraging large language models (LLMs) and embedding models, and crafting effective prompts and RAG technologies to enhance solution capabilities. The ideal candidate will build user-facing applications with frameworks such as Streamlit (React optional), integrate robust APls, and design end-to-end ML and data pipelines, with Apache Kafka for real-time data streaming and pipeline efficiency. Experience with graph databases is essential to support data structure and relationship insights. The candidate should also have expertise in cloud infrastructure with AWS, including containerization with Docker and Kubernetes for scalable deployment, and a solid understanding of MLOps practices with CI/CD automation. Strong mathematical and statistical skills, experience with data visualization, and a collaborative approach to working with cross-functional teams are crucial. Familiarity with modern ML and GenAl trends, including hands-on experience in developing cloud-based ML solutions, is essential.
JOB DESCRIPTION: Full Stack Data Scientist to develop, deploy, and manage machine learning (ML) and Generative Al (GenAl) and bedrock agents and models across a range of applications, with a focus on integrating AWS solutions. This role involves designing and optimizing predictive models, creating GenAl-driven applications leveraging large language models (LLMs) and embedding models, and crafting effective prompts and RAG technologies to enhance solution capabilities. The ideal candidate will build user-facing applications with frameworks such as Streamlit (React optional), integrate robust APls, and design end-to-end ML and data pipelines, with Apache Kafka for real-time data streaming and pipeline efficiency. Experience with graph databases is essential to support data structure and relationship insights. The candidate should also have expertise in cloud infrastructure with AWS, including containerization with Docker and Kubernetes for scalable deployment, and a solid understanding of MLOps practices with CI/CD automation. Strong mathematical and statistical skills, experience with data visualization, and a collaborative approach to working with cross-functional teams are crucial. Familiarity with modern ML and GenAl trends, including hands-on experience in developing cloud-based ML solutions, is essential.