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LLM Architect
3 weeks ago
Overview :
Seeking a visionary Lead Architect for Large Language Models (LLM) with a specialization in the development and integration of advanced NLP technologies, including Retrieval-Augmented Generation (RAG), Reinforcement Learning from Human Feedback (RLHF), safety protocols, and explainability methods.
This role demands a leader adept at navigating the forefront of AI innovation to architect LLM solutions that are not only technologically superior but also ethically sound and user centric.
Key Responsibilities :
- Architect and develop cutting-edge LLM solutions incorporating advanced technologies such as Retrieval-Augmented Generation (RAG) to enhance model performance and capability by integrating real-time information retrieval with generative models.
- Lead data collection, dataset creation, and preprocessing efforts to build comprehensive, high-quality datasets for training sophisticated LLMs, with a focus on diversity, representativeness, and ethical data use.
- Implement and refine RLHF processes to continuously improve LLM outputs based on structured human feedback, ensuring models align with user needs and ethical standards.
- Develop robust strategies for AI safety and model explainability, including mitigation of model hallucinations and biases and integration of SHAP or similar tools for model transparency and interpretability.
- Collaborate across functions to ensure that LLM architectures leverage the latest in AI research and comply with emerging ethical guidelines and regulatory standards, particularly in relation to RAG and other innovative methodologies.
- Drive innovation in LLM applications, exploring new use cases and expanding the capabilities of LLM technologies to solve complex problems in diverse domains.
Qualifications :
- Advanced degree (master's or PhD) in Computer Science, Artificial Intelligence, or a related field, with a deep focus on NLP, machine learning, and AI ethics.
- Demonstrated expertise in designing and implementing LLM architectures, with specific experience in RAG, RLHF, and modern NLP technologies.
- Proven track record of working with AI safety and explainability frameworks, adept at implementing solutions to enhance model reliability and transparency.
- Strong foundation in data science and machine learning algorithms, with experience in managing large-scale datasets for AI applications.
- Excellent leadership and communication skills, capable of driving technical innovation while fostering an ethical AI development culture.
Preferred Skills :
- Experience with other NLP and AI advancements, such as few-shot learning, transformer models, and domain adaptation techniques.
- Published research or active participation in the AI community, demonstrating thought leadership in AI safety, explainability, or LLM development.
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