GenAI Engineer

9 hours ago

Chennai, Tamil Nadu, India Qode Full time ₹ 8,00,000 - ₹ 24,00,000 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,


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Collaborate with data engineers, data scientists, and stakeholders to understand data requirements, problem statements, system integrations, and RAG application functionalities.


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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.


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Implement and optimize GenAI models for performance, scalability, and efficiency, considering factors like chunking strategies for large datasets and efficient memory management.


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Integrate GenAI models, including LLMs, into production pipelines, applications, existing analytical solutions, and RAG workflows, ensuring seamless data flow and information exchange.


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Develop user-facing interfaces and APIs (RESTful or GraphQL) to interact with GenAI models and RAG applications, providing a user-friendly experience.


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Utilize LangChain and similar tools (e.g., PromptChain) to facilitate efficient data retrieval, processing, and prompt engineering for LLM fine-tuning within RAG applications.


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Apply software engineering principles to develop robust, scalable, maintainable, and production-ready GenAI applications.


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Build and deploy GenAI applications on cloud platforms (AWS, Azure, or GCP), leveraging containerization technologies (Docker, Kubernetes) for efficient resource management.


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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.


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Continuously evaluate and improve GenAI models and applications based on data, feedback, user needs, and RAG application performance metrics.


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Stay up-to-date with the latest advancements in GenAI research, development, software engineering practices, integration tools, LLM architectures, and RAG functionalities.


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Document code, models, processes, and RAG application design for future reference and knowledge sharing

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