Paymeindia - Data Scientist - GenAI/LLM

Key Responsibilities:

- Develop, fine-tune, and deploy Large Language Models (LLMs) for various applications, including chatbots, virtual assistants, and enterprise AI solutions.

- Build and optimize conversational AI solutions with at least 1 year of experience in chatbot development.

- Implement and experiment with LLM agent development frameworks such as LangChain, LlamaIndex, AutoGen, and LangGraph .

- Design and develop ML/DL-based models to enhance natural language understanding capabilities.

- Work on retrieval-augmented generation (RAG) and vector databases (e.g., FAISS, Pinecone, Weaviate, ChromaDB) to enhance LLM-based applications.

- Optimize and fine-tune transformer-based models such as GPT, LLaMA, Falcon, Mistral, Claude, etc., for domain-specific tasks.

- Develop and implement prompt engineering techniques and fine-tuning strategies to improve LLM performance.

- Work on AI agents, multi-agent systems, and tool-use optimization for real-world business applications.

- Develop APIs and pipelines to integrate LLMs into enterprise applications.

- Research and stay up-to-date with the latest advancements in LLM architectures, frameworks, and AI trends .

Required Skills & Qualification :

- 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 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.