Lead -Applied AI Researcher
2 weeks ago
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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