Job Description
Shape the Future of Intelligence
Are you ready to architect the next generation of Artificial General Intelligence (AGI)? Nexus Horizon Technologies is seeking a visionary Senior AI Architect to lead our Project 2026 initiative. We are not just building software; we are defining the operating system for a post-silicon world.
In this role, you will bridge the gap between theoretical quantum mechanics and practical machine learning. You will lead a cross-functional team of researchers and engineers to deploy autonomous systems that redefine human-machine interaction. If you thrive in ambiguity and want to leave a legacy in the history books, this is your calling.
Responsibilities
- Architect Next-Gen Systems: Design scalable, fault-tolerant AI infrastructures capable of handling exascale computing requirements for 2026 and beyond.
- Lead Research & Development: Spearhead the integration of Quantum Computing with Deep Learning to create hybrid models with unprecedented processing speeds.
- Strategic Innovation: Identify emerging technologies and trends (e.g., Neuromorphic Computing) to drive the roadmap for our flagship product suite.
- Team Mentorship: Mentor junior architects and data scientists, fostering a culture of excellence, curiosity, and rapid prototyping.
- Model Deployment: Oversee the deployment of Large Language Models (LLMs) and reinforcement learning agents in real-world, high-stakes environments.
Qualifications
- Education: Ph.D. or Master’s degree in Computer Science, Physics, Applied Mathematics, or a related field.
- Experience: 10+ years of professional experience in machine learning, deep learning, or systems architecture.
- Tech Stack: Proficiency in Python, PyTorch, TensorFlow, and CUDA programming. Experience with quantum SDKs (e.g., Cirq, Qiskit) is a major plus.
- Leadership: Proven track record of leading high-performance engineering teams and managing complex project lifecycles from conception to delivery.
- Problem Solving: Exceptional ability to solve complex, open-ended problems with limited constraints.