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
