Large Language Model (LLM) Research & Development:
Fine-tune and optimize transformer-based models (GPT, BERT, T5, Llama, Mistral, etc.) for various business applications.
Conduct experiments on prompt engineering, fine-tuning, and parameter-efficient training methods (LoRA, QLoRA, adapters).
Design and evaluate custom loss functions, data augmentation techniques, and optimization strategies for domain-specific applications.
Retrieval-Augmented Generation (RAG) & Vector Stores:
Develop and optimize RAG architectures for enhancing model retrieval efficiency and relevance.
Work with vector databases (FAISS, Pinecone, Chroma, Milvus) for embedding search and retrieval tasks.
Implement and experiment with retrievers, re-rankers, and hybrid search techniques to improve response quality.
Model Deployment & Optimization:
Optimize LLM inference speed, memory efficiency, and cost using quantization, pruning, and distillation techniques.
Deploy LLM-based solutions on cloud (AWS, GCP, Azure) or on-prem environments, ensuring scalability and reliability.
Experiment with low-latency deployment frameworks (vLLM, DeepSpeed, Triton).
AI Experimentation & Continuous Improvement:
Stay updated with latest LLM research (e.g., OpenAI, Meta, Google DeepMind, Hugging Face).
Experiment with multi-modal AI (text, image, video, audio) and reinforcement learning to expand LLM capabilities.
Publish research findings, contribute to open-source projects, and present innovations at conferences.