Senior Data Scientist - LLMs, RAG & Multimodal AI (Remote | Immediate joiner)

Strong background in NLP, machine learning, and multimodal AI. Proven hands-on experience in LLM fine-tuning, RAG, distillation, and evaluation of foundation models. Expertise in semantic search and retrieval pipelines (e.g., FAISS, Weaviate, Vespa, Pinecone). Demonstrated ability to deploy models at scale, including distributed inference setups. Solid understanding of evaluation frameworks for ranking, retrieval, and generation. Proficiency in Python, PyTorch/TensorFlow, and modern ML toolkits. Experience in multimodal AI (bridging text, vision, or speech with LLMs). Track record of shipping latency-sensitive AI products. Strong communication skills and the ability to collaborate with cross-functional global teams.

Design, fine-tune, and optimize LLMs for applied multimodal generation use cases. Build and productionize RAG pipelines that combine embedding-based search, metadata filtering, and LLM-driven re-ranking/summarization. Apply prompt engineering, RAG techniques, and model distillation to improve grounding, reduce hallucinations, and ensure output reliability. Define and implement evaluation metrics across semantic search (nDCG, Recall@K, MRR) and generation quality (grounding accuracy, hallucination rate). Optimize inference pipelines for latency-sensitive use cases with strategies like token budgeting, prompt compression, and sub-100ms response targets. Train and adapt models via transfer learning, LoRA/QLoRA, and checkpoint reloading, ensuring robust deployment in production environments. Collaborate with product and research teams to explore innovative multimodal integrations for user-facing applications.