Job Description
<p>We are not just building for today; we are engineering the backbone of tomorrow. <strong>Nexus Future Systems</strong> is seeking a visionary <strong>Senior AI Infrastructure Architect</strong> to lead the architectural design of systems ready for the 2026 era. In this pivotal role, you will bridge the gap between cutting-edge machine learning research and robust, scalable production environments.</p>
<p>You will be responsible for defining the technical roadmap for our next-generation AI platform, ensuring our infrastructure is resilient, scalable, and optimized for the computational demands of advanced generative models. If you are passionate about solving complex engineering challenges and shaping the future of technology, we want to hear from you.</p>
Responsibilities
- <ul>
- <li>Lead the architectural design of high-throughput, low-latency distributed systems for AI workloads.</li>
- <li>Architect and implement MLOps pipelines to automate model training, validation, and deployment.</li>
- <li>Optimize inference latency and resource utilization for next-generation Large Language Models (LLMs).</li>
- <li>Collaborate with cross-functional teams (Data Science, Product, Engineering) to translate business requirements into technical specifications.</li>
- <li>Ensure system security, reliability, and compliance with industry standards (SOC2, GDPR).</li>
- <li>Drive technical strategy for cloud migration and multi-cloud strategies (AWS, GCP, Azure).</li>
- </ul>
Qualifications
- <ul>
- <li>Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.</li>
- <li>5+ years of experience in Systems Architecture, specifically within the AI/ML space.</li>
- <li>Deep expertise in Python, Go, or Rust, with strong understanding of system design principles.</li>
- <li>Proven experience managing Kubernetes clusters and container orchestration (Docker, Helm).</li>
- <li>Experience with cloud platforms (AWS, Azure, or GCP) and serverless architectures.</li>
- <li>Strong background in MLOps tools (MLflow, Kubeflow, Airflow).</li>
- </ul>