GenAI Engineer

7 days ago

Hyderabad, Telangana, India Qode Full time ₹ 1,04,000 - ₹ 13,08,780 per year
Collaborate with data engineers, data scientists, and stakeholders to understand data requirements, problem statements, and system integrations
• Utilize, apply & enhance GenAI models using state-of-the-art techniques like transformers, GANs, VAEs, and LLM models
• Implement and optimize GenAI models for performance, scalability, and efficiency
• Integrate GenAI models, including LLMs, into production pipelines, applications, and existing analytical solutions
• Develop user-facing interfaces and APIs to interact with GenAI models, including LLMs
• Utilize prompt engineering techniques to enhance model performance, including LLM models Technical skills requirements The candidate must demonstrate proficiency in,
• Collaborate with data engineers, data scientists, and stakeholders to understand data requirements, problem statements, system integrations, and RAG application functionalities.
• Use, 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.
• Implement and optimize GenAI models for performance, scalability, and efficiency, considering factors like chunking strategies for large datasets and efficient memory management.
• Integrate GenAI models, including LLMs, into production pipelines, applications, existing analytical solutions, and RAG workflows, ensuring seamless data flow and information exchange.
• Develop user-facing interfaces and APIs (RESTful or GraphQL) to interact with GenAI models and RAG applications, providing a user-friendly experience.
• Utilize LangChain and similar tools (e.g., PromptChain) to facilitate efficient data retrieval, processing, and prompt engineering for LLM fine-tuning within RAG applications.
• Apply software engineering principles to develop robust, scalable, maintainable, and production-ready GenAI applications.
• Build and deploy GenAI applications on cloud platforms (AWS, Azure, or GCP), leveraging containerization technologies (Docker, Kubernetes) for efficient resource management.
• Integrate 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.
• Continuously evaluate and improve GenAI models and applications based on data, feedback, user needs, and RAG application performance metrics.
• Stay up-to-date with the latest advancements in GenAI research, development, software engineering practices, integration tools, LLM architectures, and RAG functionalities.
• Document code, models, processes, and RAG application design for future reference and knowledge sharing

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