Generative AI Engineer

1.Design and Refine Prompts: Create, test, and optimize prompts for large language models to deliver accurate, consistent, and context-aware outputs. 2. Develop RAG Pipelines: Build and maintain Retrieval-Augmented Generation pipelines, integrating enterprise data sources and managing vector databases domains. 3. Implement NLP Solutions: Apply advanced NLP techniques (such as classification, entity extraction, summarization) and tailor models to business needs. 4. Model Training and Fine-Tuning: Train, fine-tune, and evaluate LLMs and custom models using enterprise framework and tools. 5. LLMOps Integration: Deploy and monitor LLM workflows using LLMOps platforms like Arize or Langfuse, continuously tracking model performance, drift, errors, and user interactions for optimal outcomes. 6. Deploy and Monitor Solutions: Package, deploy, and monitor GenAI applications on Azure, optimizing for reliability, latency, and compliance. 7. Hands-on Python Development: Leverage Python for scripting, data processing, model development, and integration tasks throughout the AI solution lifecycle. 8. Collaborate and Document: Work with cross-functional teams to align technical solutions with business goals, and maintain thorough technical documentation while ensuring security, data privacy, and responsible AI standards