Job Description
Are you ready to shape the technological landscape of 2026? Nebula Dynamics is seeking a visionary Senior AI Infrastructure Architect to lead the development of our next-generation autonomous systems and generative AI frameworks. We are building the backbone of the future, and we need a technical expert who thrives on complexity and innovation.
In this pivotal role, you will architect high-performance computing solutions that scale to meet the demands of next-gen artificial intelligence. You will bridge the gap between theoretical research and production-grade systems, ensuring our AI models are efficient, secure, and capable of real-time processing.
Responsibilities
- Architect Next-Gen Infrastructure: Design and deploy scalable, fault-tolerant AI infrastructure optimized for large-scale model training and inference.
- System Optimization: Lead initiatives to optimize latency and throughput for generative AI models, focusing on edge computing and distributed systems.
- Collaborative Engineering: Partner with data scientists and ML engineers to translate research into robust, production-ready software.
- Security & Compliance: Implement rigorous security protocols to protect proprietary algorithms and ensure data sovereignty in cloud environments.
- Technical Leadership: Mentor a team of engineers, establishing coding standards and best practices for AI-driven development.
- Future-Proofing: Research and prototype emerging technologies (e.g., quantum-resistant cryptography, neuromorphic computing) for integration into the 2026 roadmap.
Qualifications
- Experience: 8+ years of experience in software engineering, with at least 4 years specifically focused on Machine Learning infrastructure or distributed systems.
- Programming Mastery: Deep expertise in Python, Rust, or Go, with experience in systems-level programming.
- Cloud & Containers: Proven track record of designing solutions on major cloud providers (AWS/GCP/Azure) using Kubernetes and Docker.
- AI Stack: Familiarity with deep learning frameworks (PyTorch, TensorFlow) and experience managing GPU clusters or TPU environments.
- Problem Solving: Strong ability to troubleshoot complex performance bottlenecks in high-concurrency environments.
- Education: Bachelor’s degree in Computer Science, Engineering, or a related field; Master’s degree preferred.