Lead -Applied AI Researcher

5 days ago

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Mission Statement As Applied AI Researcher, you will define and execute a research-intensive AI and data science agenda. You will lead foundational model research, drive agentic AI innovation, and publish advances that propel organizational leadership in enterprise-grade AI agent solutions. Role Overview - Own the foundation model and agentic AI research roadmap, setting strategic direction for breakthrough innovations. - Lead peer-reviewed research, drive early-stage experimentation, and steward integration of research outcomes into advanced agentic AI products and platforms. - Mentor and inspire an elite research-oriented data science team. Research Leadership - Shape and drive original research in foundation models, large-scale generative architectures, and agentic reasoning. - Lead benchmarking, adaptation, and application of state-of-the-art generative models (e.g., LLMs, multimodal transformers) for new product capabilities. - Publish high-impact research; foster industry-academic collaborations and represent Flytxt at AI conferences and global fora. - Prototype, validate, and transfer novel agentic AI methods into enterprise contexts. Strategic Responsibilities - Develop research frameworks, open science practices, and model governance for responsible AI innovation. - Guide cross-functional research teams to integrate foundational advances into the organization’s agentic AI platforms (e.g., Niya-X). - Advise on strategic investments in AI infrastructure and drive adoption of pioneering algorithms and architectures. Qualifications & Experience - Min 10 Years of progressive experience with below technical capabilities and prescribed qualifications - PhD (preferred) or Master’s in Machine Learning, AI, Computer Science, Mathematics, or a related quantitative discipline. - Peer-reviewed publications in top AI/ML conferences or journals. Domain and Technical Skills - Hands-on expertise with AI development environments - Experience developing, evaluating, and scaling foundation models such as LLMs or multimodal transformers. - Familiarity with rapid prototyping and product development lifecycles in an innovation-driven research environment. - Applied AI research experience. Behavioral Competencies - Strong analytical, mentoring, and cross-team leadership skills. - Ability to mentor, influence, and collaborate across multicultural, interdisciplinary research teams. - Strong logical reasoning, decision-making, and strategic vision skills.

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