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 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 effective 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

📌 Genai Engineer (Tamil Nadu)
🏢 Qode
📍 Tamil Nadu