Data Scientist

• 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.

Senior Data Scientist – LLMs, RAG, Multimodal AI

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