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Data Scientist with AWS Bedrock

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About the position

Responsibilities

  • Architect and implement enterprise-scale generative AI solutions using AWS Bedrock and SageMaker.
  • Design and develop custom LLM applications leveraging foundation models available in Bedrock.
  • Lead model fine-tuning, prompt engineering, and optimization initiatives.
  • Build end-to-end GenAI pipelines using AWS services and implement RAG (Retrieval Augmented Generation) architectures.
  • Develop custom embeddings and vector search solutions.
  • Create production-ready AI applications with proper monitoring and governance.
  • Collaborate with data scientists and data engineers to integrate AI applications with existing systems and utilize the existing pipelines, building new pipelines to support the same.
  • Provide guidance and best practices on AI architectures, design patterns, and implementation methodologies.
  • Stay up to date with the latest advancements in AI technology and AWS services and drive innovation by exploring and implementing new use cases using appropriate tools and services.
  • Excellent communication and interpersonal skills to collaborate with data scientists, engineers, and other key stakeholders.
  • Work well in a fast-paced cross-functional environment.

Requirements

  • Proven experience as an AI architect with a focus on building performant AI solutions for multiple clients across different domains using the AWS platform. Experience in the Energy Utilities domain would be a plus.
  • Deep understanding of Amazon Bedrock foundation models, SageMaker features and capabilities, AWS AI/ML services ecosystem, and model deployment and serving architectures.
  • Strong experience in machine learning, deep learning, natural language processing, and computer vision techniques.
  • Proficiency in using AI frameworks such as TensorFlow, PyTorch, MXNet using programming languages such as Python, Java, Julia, Rust.
  • Experience with containerization using Docker and orchestration using Kubernetes.
  • Using infrastructure automation using CloudFormation or Terraform.
  • Experience with machine learning frameworks, libraries, and agile environments.
  • Experience with version control tools such as Git, Bitbucket, etc., and knowledge of Docker, Jenkins, Kubernetes, and other DevOps tools.
  • Strong communication and interpersonal skills with the ability to collaborate effectively with cross-functional teams and other stakeholders.
  • Outstanding analytical and problem-solving skills.
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