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 effective 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

📌 Genai Engineer (Telangana)
🏢 Qode
📍 Telangana