Data Scientist

As an AI Language Model Developer, you will be responsible for developing, fine-tuning, and deploying Large Language Models (LLMs) for various applications such as chatbots, virtual assistants, and enterprise AI solutions. Your key responsibilities will include: - Building and optimizing conversational AI solutions with a minimum of 1 year of experience in chatbot development. - Implementing and experimenting with LLM agent development frameworks like LangChain, LlamaIndex, AutoGen, and LangGraph. - Designing and developing ML/DL-based models to enhance natural language understanding capabilities. - Working on retrieval-augmented generation (RAG) and vector databases such as FAISS, Pinecone, Weaviate, ChromaDB to improve LLM-based applications. - Optimizing and fine-tuning transformer-based models like GPT, LLaMA, Falcon, Mistral, Claude, etc., for domain-specific tasks. - Developing and implementing prompt engineering techniques and fine-tuning strategies to enhance LLM performance. - Working on AI agents, multi-agent systems, and tool-use optimization for real-world business applications. - Developing APIs and pipelines to integrate LLMs into enterprise applications. - Researching and staying up-to-date with the latest advancements in LLM architectures, frameworks, and AI trends. Qualifications and Skills Required: - 1-4 years of experience in Machine Learning (ML), Deep Learning (DL), and NLP-based model development. - Hands-on experience in developing and deploying conversational AI/chatbots is a plus. - Strong proficiency in Python and experience with ML/DL frameworks such as TensorFlow, PyTorch, and Hugging Face Transformers. - Experience with LLM agent development frameworks like LangChain, LlamaIndex, AutoGen, LangGraph. - Knowledge of vector databases (e.g., FAISS, Pinecone, Weaviate, ChromaDB) and embedding models. - Understanding of Prompt Engineering and Fine-tuning LLMs. - Familiarity with cloud services (AWS, GCP, Azure) for deploying LLMs at scale. - Experience in working with APIs, Docker, FastAPI for model deployment. - Strong analytical and problem-solving skills. - Ability to work independently and collaboratively in a fast-paced environment. Good to Have: - Experience with Multi-modal AI models (text-to-image, text-to-video, speech synthesis, etc.). - Knowledge of Knowledge Graphs and Symbolic AI. - Understanding of MLOps and LLMOps for deploying scalable AI solutions. - Experience in automated evaluation of LLMs and bias mitigation techniques. - Research experience or published work in LLMs, NLP, or Generative AI is a plus. (Note: This job description is based on the provided information and is not exhaustive. For more details, please refer to the source: hirist.tech),