GenAI Engineer

Telangana
Permanent
Full-time

28 days ago
Apply easily

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 requirementsThe 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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