jobTitle

skills

GenAI Engineer

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