Sr. Data Scientist

6 days ago

Bengaluru, Karnataka, India Yash Technologies Full time ₹ 20,00,000 - ₹ 25,00,000 per year

We are looking forward to hirePython Professionals in the following areas :

Technologies: Amazon Bedrock, RAG Models, Java, Python, C or C++, AWS Lambda,

Responsibilities:

  • Responsible for developing, deploying, and maintaining a Retrieval Augmented Generation (RAG) model in Amazon Bedrock, our cloud-based platform for building and scaling generative AI applications.
  • Design and implement a RAG model that can generate natural language responses, commands, and actions based on user queries and context, using the Anthropic Claude model as the backbone.
  • Integrate the RAG model with Amazon Bedrock, our platform that offers a choice of high-performing foundation models from leading AI companies and Amazon via a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.
  • Optimize the RAG model for performance, scalability, and reliability, using best practices and robust engineering methodologies.
  • Design, test, and optimize prompts to improve performance, accuracy, and alignment of large language models across diverse use cases.
  • Develop and maintain reusable prompt templates, chains, and libraries to support scalable and consistent GenAI applications.

Skills/Qualifications:

  • Experience in programming with at least one software language, such as Java, Python, or C/C++.
  • Experience in working with generative AI tools, models, and frameworks, such as Anthropic, OpenAI, Hugging Face, TensorFlow, PyTorch, or Jupyter.
  • Experience in working with RAG models or similar architectures, such as RAG, Ragna, or Pinecone.
  • Experience in working with Amazon Bedrock or similar platforms, such as AWS Lambda, Amazon SageMaker, or Amazon Comprehend.
  • Ability to design, iterate, and optimize prompts for various LLM use cases (e.g., summarization, classification, translation, Q&A, and agent workflows).
  • Deep understanding of prompt engineering techniques (zero-shot, few-shot, chain-of-thought, etc.) and their effect on model behavior.
  • Familiarity with prompt evaluation strategies, including manual review, automatic metrics, and A/B testing frameworks.
  • Experience building prompt libraries, reusable templates, and structured prompt workflows for scalable GenAI applications.
  • Ability to debug and refine prompts to improve accuracy, safety, and alignment with business objectives.
  • Awareness of prompt injection risks and experience implementing mitigation strategies.
  • Familiarity with prompt tuning, parameter-efficient fine-tuning (PEFT), and prompt chaining methods.
  • Familiarity with continuous deployment and DevOps tools preferred. Experience with Git preferred
  • Experience working in agile/scrum environments
  • Successful track record interfacing and communicating effectively across cross-functional teams.
  • Good communication, analytical and presentation skills, problem-solving skills and learning attitude

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