Data Scientist -2 (LLM/GenAI)

Key Responsibilities: Develop, fine-tune, and deployLarge Language Models (LLMs)for various applications, including chatbots, virtual assistants, and enterprise AI solutions. Build and optimizeconversational AI solutionswithat least 1 yearof experience in chatbot development. Implement and experiment withLLM agent development frameworkssuch asLangChain, LlamaIndex, AutoGen, and LangGraph. Design and developML/DL-based modelsto enhance natural language understanding capabilities. Work onretrieval-augmented generation (RAG)andvector databases(e.g., FAISS, Pinecone, Weaviate, ChromaDB) to enhance LLM-based applications. Optimize and fine-tune transformer-based models such asGPT, LLaMA, Falcon, Mistral, Claude, etc.for domain-specific tasks. Develop and implementprompt engineering techniquesandfine-tuning strategiesto improve LLM performance. Work onAI agents, multi-agent systems, and tool-use optimizationfor real-world business applications. Develop APIs and pipelines to integrate LLMs into enterprise applications. Research and stay up to date with the latest advancements inLLM architectures, frameworks, and AI trends. RequirementsRequired Skills Qualifications: 3-5 years of experiencein Machine Learning (ML), Deep Learning (DL), and NLP-based model development. Hands-on experiencein developing and deploying conversational AI/chatbots is Plus Strong proficiency inPythonand experience with ML/DL frameworks such asTensorFlow, PyTorch, Hugging Face Transformers. Experience withLLM agent development frameworkslikeLangChain, LlamaIndex, AutoGen, LangGraph. Knowledge ofvector databases(e.g., FAISS, Pinecone, Weaviate, ChromaDB) andembedding models. Understanding ofPrompt EngineeringandFine-tuning LLMs. Familiarity withcloud services (AWS, GCP, Azure)for deploying LLMs at scale. Experience in working withAPIs, Docker, FastAPIfor model deployment. Strong analytical and problem-solving skills. Ability to work independently and collaboratively in a fast-paced environment. Good to Have: Experience withMulti-modal AI models (text-to-image, text-to-video, speech synthesis, etc.). Knowledge ofKnowledge GraphsandSymbolic AI. Understanding ofMLOpsandLLMOpsfor deploying scalable AI solutions. Experience inautomated evaluation of LLMsandbias mitigation techniques. Research experience or published work in