Vision: Enterprises operating on institutional intelligence—governed, self-improving, and scalable beyond individual expertise.
JazzX AI is defining the future of enterprise work—by building AI-native digital workers that actually get the job done.
We believe enterprises don't scale expertise—they lose it. Knowledge stays trapped in individuals, judgment gets applied inconsistently, and the best talent spends time on work that should run itself. We're changing that.
JazzX AI transforms messy enterprise reality into institutional intelligence: governed digital workers that capture expert judgment, make every decision explainable, and continuously improve through real-world execution. The result is faster decisions, higher-quality outcomes, and reliable execution at scale—in domains where getting it wrong isn't an option.
We're starting with lending and due-diligence workflows—complex, regulated, high-stakes. From here, we're building the backbone for enterprise intelligence across industries.
This is early-stage, hard, and consequential work. If you want to be part of bringing AI systems to market that actually run in production, handle real complexity, and deliver real outcomes—not demos, not chatbots—JazzX AI is the place.
SAIGroup a private investment firm that has committed $1B to build and scale next-generation, AI-powered enterprise software companies. SAIGroup’s portfolio serves 2,000+ global enterprise customers, generates nearly $800M in annual revenue, and employs 4,000+ people worldwide — providing JazzX AI with long-term capital, deep operating expertise, and access to real-world enterprise scale from day one.
We are seeking an experienced AI Engineer with deep expertise in Reinforcement Learning (RL) to join our team as a Senior Staff Architect. In this role, you will be responsible for shaping the vision, architecture, and technical execution of RL-driven AI reasoning models and systems that power next-generation enterprise AGI platform.
You will lead the design, development, and optimization of cutting-edge RL solutions, from experimentation and simulation through production deployment. This includes building scalable training architectures, architecting multi-agent and hierarchical RL