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 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.