Job Description
Are you ready to architect the intelligence of tomorrow?
Nexus Horizon is seeking a visionary Senior AI Architect to lead the development of our next-generation generative AI infrastructure. As we push the boundaries of what is possible in 2026, you will be at the forefront of building scalable, secure, and high-performance AI systems that redefine human-computer interaction.
Why Join Nexus Horizon?
• Future-Proof Technology: Work on cutting-edge Large Language Models (LLMs) and multimodal AI systems.
• Elite Team: Collaborate with top-tier researchers and engineers from leading tech institutions.
• Impact: Your work will directly shape the AI landscape for the next decade.
The Role
In this role, you will bridge the gap between theoretical AI research and production-grade software engineering. You will design the architectural frameworks that allow our AI models to learn, adapt, and deploy efficiently in real-world environments.
Responsibilities
- Design Scalable AI Infrastructure: Architect high-throughput, low-latency systems for training and serving large-scale generative models.
- Optimize Model Performance: Implement advanced quantization and pruning techniques to reduce inference costs while maintaining accuracy.
- Collaborate with Research Teams: Translate research findings into robust production pipelines and APIs.
- Security & Compliance: Establish best-in-class data governance protocols and privacy-preserving AI architectures.
- Technical Leadership: Mentor junior engineers and drive architectural decisions across the organization.
- Future-Proofing: Evaluate emerging AI paradigms (e.g., Neuromorphic computing) and integrate them into our core stack where applicable.
Qualifications
- Education: Master’s or PhD in Computer Science, Artificial Intelligence, or a related technical field.
- Experience: 8+ years of experience in software engineering with a specific focus on Machine Learning or Deep Learning.
- Technical Stack: Proficiency in Python, PyTorch, TensorFlow, and distributed computing frameworks (Ray, Kubernetes).
- Cloud Mastery: Extensive experience deploying models on AWS, Azure, or GCP.
- Architectural Skills: Deep understanding of MLOps, CI/CD for AI, and microservices architecture.
- Problem Solving: Proven track record of solving complex engineering challenges in high-stakes environments.