Job Description
Are you ready to architect the operating system for the next decade?
At Aethelgard Systems, we aren't just building software; we are constructing the cognitive layer of the future. As we pivot toward our 2026 roadmap, we are seeking a visionary Lead Quantum AI Architect to spearhead the integration of quantum computing paradigms with deep neural networks. This is a rare opportunity to define the technical foundation of high-impact AI systems that will redefine human-machine interaction.
You will be at the intersection of theoretical physics and practical engineering, leading a world-class team to solve problems previously thought impossible. If you are driven by complexity and want to leave a legacy in the tech industry, apply today.
Responsibilities
- Architectural Vision: Design and implement scalable, high-performance quantum neural network architectures optimized for near-term quantum hardware.
- System Design: Lead the end-to-end technical strategy for our AI infrastructure, ensuring seamless integration with legacy systems and cloud ecosystems.
- Team Leadership: Mentor and guide a diverse team of ML engineers, quantum physicists, and data scientists, fostering a culture of innovation and technical excellence.
- R&D Leadership: Stay at the forefront of quantum algorithm research (e.g., QAOA, VQE) and apply cutting-edge techniques to real-world business problems.
- Prototyping: Rapidly prototype and validate novel AI models using Python and quantum simulators before full-scale deployment.
- Stakeholder Management: Translate complex technical concepts into clear strategies for executive leadership and cross-functional partners.
Qualifications
- Education: PhD or Master’s degree in Computer Science, Computational Physics, Applied Mathematics, or a related field.
- Experience: 7+ years of experience in AI/ML architecture, with at least 3 years in a leadership or senior architect role.
- Tech Stack: Proficiency in Python, C++, and experience with quantum computing frameworks (Qiskit, Cirq, PennyLane, or PyTorch Quantum).
- Knowledge: Deep understanding of classical machine learning algorithms and their quantum counterparts (variational circuits, quantum feature maps).
- Soft Skills: Exceptional communication skills with the ability to bridge the gap between theoretical research and practical engineering.
- Passion: Demonstrated passion for the future of technology and the potential of quantum computing to solve unsolvable problems.