Job Description
We are seeking a visionary Senior AI Architect to spearhead Project 2026, our groundbreaking initiative to redefine the boundaries of artificial general intelligence. At Nexus Horizon Systems, we don't just predict the future; we build it. If you are a technical leader passionate about solving complex, unsolved problems and shaping the digital landscape of tomorrow, we want to hear from you.
In this pivotal role, you will architect scalable, high-performance AI systems and lead a world-class engineering team. You will work at the intersection of deep learning, quantum computing interfaces, and ethical AI frameworks. Join us in crafting the infrastructure that will power the next generation of intelligent applications.
Responsibilities
- Design and architect end-to-end AI solutions that drive business innovation and efficiency for Project 2026.
- Lead the technical strategy for deploying large-scale machine learning models in production environments.
- Collaborate with cross-functional teams including data scientists, researchers, and product managers to define technical requirements.
- Mentor and develop high-performing engineering talent, fostering a culture of technical excellence and continuous learning.
- Ensure system scalability, reliability, and security across distributed cloud infrastructure.
- Stay at the forefront of AI research to integrate cutting-edge advancements into our core products.
Qualifications
- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or a related field; PhD preferred.
- 10+ years of experience in software engineering, with at least 5 years in a senior architecture or technical leadership role.
- Deep expertise in Python, TensorFlow, PyTorch, and modern cloud platforms (AWS, GCP, or Azure).
- Proven track record of designing and implementing high-availability systems serving millions of users.
- Experience with distributed systems, microservices architecture, and containerization (Docker, Kubernetes).
- Strong understanding of machine learning lifecycles, MLOps, and data pipeline architecture.