GenAI Engineer

·      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

·      Apply software engineering principles to develop robust, scalable, and maintainable GenAI applications

·      Build and deploy GenAI applications on cloud platforms

·      Integrate GenAI applications with other applications, tools, and analytical solutions to create a cohesive user experience and workflow

·      Continuously evaluate and improve GenAI models and applications based on data, feedback, and user needs

·      Stay up-to-date with the latest advancements in GenAI research, development, software engineering practices, and integration tools

·      Document code, models, and processes for future reference

·      Build and maintain tools and infrastructure for data processing for AI/ML development initiatives.

 

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.

·      Experience working with RAG applications

·      Experience with cloud-based data warehousing solutions (e.g., BigQuery, Redshift, Snowflake)

·      Experience with cloud-based workflow orchestration tools (e.g., Airflow, Prefect)

·      Familiarity with Kubernetes (K8S) is a welcome addition

·      Google Cloud certification

·      Unix or Shell scripting