Job Description

Collaborate with data engineers, data scientists, and stakeholders to understand data requirements, problem statements, and system integrationso Utilize, apply & enhance GenAI models using state-of-the-art techniques like transformers, GANs, VAEs, and LLM modelso Implement and optimize GenAI models for performance, scalability, and efficiencyo Integrate GenAI models, including LLMs, into production pipelines, applications, and existing analytical solutionso Develop user-facing interfaces and APIs to interact with GenAI models, including LLMso Utilize prompt engineering techniques to enhance model performance, including LLM modelsTechnical skills requirementsThe candidate must demonstrate proficiency in,oCollaborate with data engineers, data scientists, and stakeholders to understand data requirements, problem statements, system integrations, and RAG application functionalities.oUse, apply, enhance GenAI models using state-of-the- art techniques like transformers, GANs, VAEs, LLMs (including experience with various LLM architectures and capabilities), and vector representations for efficient data processing.oImplement and optimize GenAI models for performance, scalability, and efficiency, considering factors like chunking strategies for large datasets and efficient memory management.oIntegrate GenAI models, including LLMs, into production pipelines, applications, existing analytical solutions, and RAG workflows, ensuring seamless data flow and information exchange.oDevelop user-facing interfaces and APIs (RESTful or GraphQL) to interact with GenAI models and RAG applications, providing a user-friendly experience.oUtilize LangChain and similar tools (e.g., PromptChain) to facilitate efficient data retrieval, processing, and prompt engineering for LLM fine-tuning within RAG applications.oApply software engineering principles to develop robust, scalable, maintainable, and production-ready GenAI applications.oBuild and deploy GenAI applications on cloud platforms (AWS, Azure, or GCP), leveraging containerization technologies (Docker, Kubernetes) for efficient resource management.oIntegrate GenAI applications with other applications, tools, and analytical solutions (including dashboards and reporting tools) to create a cohesive user experience and workflow within the RAG ecosystem.oContinuously evaluate and improve GenAI models and applications based on data, feedback, user needs, and RAG application performance metrics.oStay up-to-date with the latest advancements in GenAI research, development, software engineering practices, integration tools, LLM architectures, and RAG functionalities.oDocument code, models, processes, and RAG application design for future reference and knowledge sharing