Data Scientist +gen AI+prompt engineering+RAG

Job Description Data Scientist (Generative AI) Job Title: Data Scientist Generative AI (GenAI) Location: Hybrid Employment Type: Full-Time Noitice Period : Only Aug Joiners , 15 days Job Summary We are seeking a highly skilled Data Scientist Generative AI with strong expertise in Data Science, Large Language Models (LLMs), Prompt Engineering, and Retrieval-Augmented Generation (RAG) . The ideal candidate should have hands-on experience in designing, developing, and deploying GenAI solutions to solve real-world business problems. Strong communication and stakeholder management skills are essential. Key Responsibilities Design, build, and deploy Generative AI applications using LLMs. Develop and optimize RAG (Retrieval-Augmented Generation) pipelines for enterprise use cases. Create, evaluate, and optimize prompts to improve LLM performance. Apply Data Science and Machine Learning techniques to solve complex business challenges. Fine-tune and evaluate AI models for accuracy, scalability, and efficiency. Work closely with business stakeholders to understand requirements and translate them into AI solutions. Integrate GenAI solutions with enterprise applications and data sources. Stay updated with the latest advancements in Generative AI, LLMs, and AI frameworks. Required Skills Strong hands-on experience in Data Science . Practical experience with Generative AI and Large Language Models (LLMs) . Strong expertise in Prompt Engineering . Experience building RAG (Retrieval-Augmented Generation) solutions. Proficiency in Python and AI/ML libraries. Experience with vector databases and embedding models is preferred. Strong analytical and problem-solving skills. Excellent verbal and written communication skills. Preferred Skills Experience with LangChain, LangGraph, LlamaIndex, or similar frameworks. Knowledge of OpenAI, Azure OpenAI, Anthropic, or Google Gemini models. Experience with vector databases such as Pinecone, FAISS, ChromaDB, or Milvus. Exposure to cloud platforms such as Azure, AWS, or GCP.