Job Description
Shape the Future of Intelligence. Nexus Horizon is a pioneer in next-generation AI infrastructure. We are currently seeking a visionary Lead AI Systems Architect to lead our engineering efforts into the 2026 era. If you possess the foresight to build systems that scale beyond current limitations and the technical prowess to implement them, we want to hear from you.
In this role, you will be the technical architect behind our flagship generative AI platforms. You won't just maintain existing systems; you will define the blueprints for the autonomous agents and neural networks that will define the next decade of technology. Join us in building the backbone of the AI revolution.
Responsibilities
- Design Next-Gen Architectures: Spearhead the design and implementation of scalable, fault-tolerant AI infrastructure designed for 2026 workloads, including quantum-ready algorithms and edge computing nodes.
- Lead Research Integration: Translate high-level AI research into production-grade software, optimizing models for latency, throughput, and cost-efficiency.
- System Optimization: Drive performance engineering initiatives to ensure our systems handle petabyte-scale data streams with sub-millisecond latency.
- Talent Development: Mentor a high-performing team of ML engineers and data scientists, fostering a culture of innovation and technical excellence.
- Strategic Planning: Collaborate with C-level executives to define long-term technical roadmaps and infrastructure strategies aligned with market trends.
- Cross-Functional Collaboration: Work closely with product managers and data scientists to ensure architectural solutions meet business requirements.
Qualifications
- Education: Master’s or Ph.D. in Computer Science, Artificial Intelligence, or a related engineering discipline.
- Experience: 8+ years of experience in software engineering, with at least 5 years specifically in AI/ML infrastructure and distributed systems architecture.
- Technical Mastery: Expert proficiency in Python, C++, and deep learning frameworks (PyTorch, TensorFlow). Experience with Kubernetes, Docker, and cloud-native technologies (AWS/GCP/Azure).
- Problem Solving: Proven track record of solving complex engineering challenges, including large-scale optimization and system reliability.
- Leadership: Demonstrated ability to lead technical teams and influence engineering culture from the ground up.