Role and responsibilities
· 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.
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.
Nice-to-have skills
· 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
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