Job Description
We are on the precipice of a technological revolution. Nebula Systems is seeking a visionary Future Systems Architect to lead the design and implementation of our roadmap for 2026 and beyond. If you are passionate about building scalable, resilient, and intelligent infrastructure that defines the future of computing, we want to hear from you.
In this role, you will bridge the gap between current cloud technologies and the next generation of AI-driven infrastructure. You will not just maintain systems; you will architect the very fabric of our digital ecosystem, ensuring we are ready for the demands of the coming decade.
Responsibilities
- Lead the 2026 Roadmap: Define and execute the technical vision for our next-generation infrastructure, focusing on scalability, quantum-ready architectures, and AI integration.
- System Architecture: Design robust, fault-tolerant microservices and distributed systems that can handle high-concurrency workloads and future hardware advancements.
- Cloud Optimization: Oversee the migration and optimization of our cloud-native stack, leveraging Kubernetes, serverless, and edge computing paradigms.
- AI Infrastructure: Collaborate with ML engineers to build and deploy the infrastructure required for large-scale neural network training and inference.
- Cross-Functional Leadership: Mentor senior engineering teams and collaborate with product managers to translate futuristic requirements into actionable technical specifications.
- Security & Compliance: Implement cutting-edge security protocols and ensure our systems remain compliant with evolving data privacy regulations.
Qualifications
- Experience: 7+ years of experience in systems architecture, DevOps, or software engineering, with at least 2 years in a lead capacity.
- Core Skills: Deep expertise in cloud platforms (AWS, Azure, or GCP), containerization (Docker/Kubernetes), and infrastructure-as-code (Terraform/Ansible).
- Programming: Proficiency in Python, Go, or Rust for scripting and tooling development.
- AI/ML Knowledge: Understanding of MLOps, data pipelines, and the infrastructure requirements of machine learning models.
- Problem Solving: Exceptional ability to troubleshoot complex, distributed system failures and optimize performance bottlenecks.
- Education: Bachelor’s degree in Computer Science, Engineering, or a related field (Master’s preferred).