Job Description
Join the Architects of Tomorrow.
Quantum Horizon is at the forefront of defining the technological landscape of 2026 and beyond. We are seeking a visionary Lead AI Architect to spearhead our Project 2026 initiative—a revolutionary platform designed to integrate generative AI with autonomous decision-making systems.
In this role, you will not merely implement existing solutions; you will design the foundational architecture for next-generation neural networks. You will work in a high-performance environment where innovation is not just encouraged—it is the metric of success. If you are passionate about the future of artificial intelligence and have the technical prowess to build scalable, robust systems, we want to meet you.
As a key leader on our team, you will bridge the gap between theoretical research and production-grade engineering, ensuring our solutions are ethical, efficient, and scalable.
Responsibilities
- Architectural Leadership: Design and oversee the development of complex AI infrastructure, focusing on scalability, security, and high-performance computing.
- Roadmap Strategy: Define the technical roadmap for Project 2026, aligning AI capabilities with long-term business objectives.
- Team Mentorship: Lead, mentor, and coach a diverse team of data scientists and software engineers, fostering a culture of technical excellence.
- Model Optimization: Drive research and implementation of state-of-the-art machine learning models, including Large Language Models (LLMs) and computer vision systems.
- System Integration: Integrate AI solutions seamlessly into existing product ecosystems, ensuring interoperability and data flow.
- Stakeholder Communication: Translate complex technical concepts into clear insights for non-technical stakeholders and executive leadership.
- Best Practices: Establish and enforce engineering standards, code reviews, and CI/CD pipelines for AI workflows.
Qualifications
- Education: Master’s or Ph.D. in Computer Science, Artificial Intelligence, Robotics, or a related field.
- Experience: 10+ years of experience in software engineering, with at least 5 years in AI/ML architecture and leadership roles.
- Technical Stack: Deep expertise in Python, TensorFlow, PyTorch, and distributed computing frameworks (Kubernetes, Docker).
- Domain Knowledge: Proven experience with Natural Language Processing (NLP), deep learning, and reinforcement learning.
- Leadership: Demonstrated ability to lead high-performance engineering teams through complex, ambiguous challenges.
- Problem Solving: Exceptional analytical skills with a track record of solving unsolved problems in AI scalability.
- Communication: Excellent verbal and written communication skills, capable of articulating technical vision to a global audience.