Job Description
Quantum Core Systems is pioneering the next generation of artificial intelligence infrastructure. We are looking for a visionary Senior AI Architect (2026 Vision) to lead our engineering team in building scalable, resilient, and ethical AI systems that will define the landscape of technology in the coming decade.
In this role, you will bridge the gap between theoretical AI research and practical, large-scale deployment. You will be responsible for architecting the core neural networks that power our proprietary platforms, ensuring they are future-proofed for the rapid evolution of AI capabilities by 2026 and beyond.
Why Join Us?
We offer a competitive package, remote-first flexibility, and the opportunity to work on projects that genuinely impact the future of human-machine interaction.
Responsibilities
- Design and architect end-to-end AI infrastructure capable of handling petabyte-scale data processing.
- Lead the development and deployment of Large Language Models (LLMs) and generative AI agents.
- Collaborate with cross-functional teams of data scientists, engineers, and product managers to define technical roadmaps.
- Ensure system scalability, security, and compliance with global AI regulations and ethical standards.
- Conduct technical research to identify emerging AI trends and integrate them into our core architecture.
- Mentor junior architects and engineers, fostering a culture of innovation and technical excellence.
- Oversee the integration of edge computing solutions to enhance real-time AI processing capabilities.
Qualifications
- Master’s or PhD in Computer Science, Artificial Intelligence, or a related technical field (or equivalent practical experience).
- 10+ years of experience in software engineering with a focus on AI/ML systems architecture.
- Proven expertise in designing and implementing deep learning frameworks (TensorFlow, PyTorch, JAX).
- Strong proficiency in cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- Experience with distributed systems, microservices architecture, and high-availability infrastructure.
- Deep understanding of MLOps practices, including model versioning, CI/CD for ML, and A/B testing.
- Excellent communication skills with the ability to translate complex technical concepts for diverse stakeholders.