AI Scientist

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Requirements:

• Advanced degree (Ph.D. or Master's) in Computer Science, AI, or related field with a research focus.
• 3+ years of AI/ML research experience, specializing in NLP and Generative AI.
• Deep understanding of LLMs, agent architectures, and reinforcement learning.
• Proficient in Python and foundational frameworks and libraries (e.g., PyTorch, TensorFlow).
• Experience with LLM-related NLP techniques (transformers, attention mechanisms), data analysis, statistical modeling, and ML evaluation metrics.
• Experience with vector search and search optimization techniques for RAG/CRAG.
• Experience with LangChain/LangGraph (or similar) and LLM training/deployment on cloud infrastructure (AWS, GCP, Azure preferred).
• Strong analytical, problem-solving, communication, and collaboration skills; ability to work independently and in teams. Healthcare data/revenue cycle experience a strong plus.

Responsibilities:

• Conduct cutting-edge research on LLMs and Generative AI agents to automate key revenue cycle processes, including exploring novel agent architectures (hierarchical, multi-agent, reinforcement learning-based).
• Develop and implement advanced prompt engineering techniques to optimize LLM behavior within complex agent workflows.
• Design, implement, and analyze experiments to evaluate and optimize LLM and agent performance on customer-focused and infrastructure tasks, focusing on robustness, explainability, adaptability, and bias mitigation for responsible use.
• Utilize frameworks like LangChain or LangGraph to build and experiment with complex agent workflows.
• Collaborate with engineering, ML Ops, and product teams to research and develop Generative AI training infrastructure and data pipelines.
• Stay current with LLM and agent research, sharing findings, contributing to AI strategy, and guiding offshore teams on technical requirements and POCs.
• Document research methods, findings, and recommendations, and transition research prototypes into deployable solutions.