Lead research and development initiatives in the GenAI domain, focusing on Large Language Models, Retrieval-Augmented Generation, and autonomous agents
Design and implement advanced workflows for integrating LLMs into real-world applications across Finance, Insurance, and Healthcare
Develop and fine-tune domain-specific LLMs using prompt engineering, adapter-based tuning, or low-rank adaptation
Architect and implement scalable knowledge graph solutions to enhance reasoning capabilities
Drive development of retrieval-augmented systems combining LLMs with document retrieval, clustering, and search techniques
Collaborate with cross-functional teams to prototype, test, and deploy solutions in production (on-premise and cloud)
Mentor and guide junior researchers and engineers and document research findings and implementations
Requirements
Bachelor's Degree
Experience in Large Language Models (LLMs) and GenAI research
Experience with Retrieval-Augmented Generation (RAG) and document retrieval techniques
Knowledge of prompt engineering, adapter-based tuning, or low-rank adaptation
Experience architecting and implementing knowledge graph solutions
Experience with clustering and search techniques
Experience deploying solutions in production on-premise and in the cloud
Ability to mentor and guide junior researchers and engineers
Ability to read, adapt, and implement cutting-edge research
Strong collaboration with cross-functional teams and prototyping skills
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