Lead -Applied AI Researcher

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 organizations 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 Masters 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.