Job Description
Are you ready to architect the digital landscape of tomorrow? Apex Future Systems is seeking a visionary Senior AI Infrastructure Architect to lead our mission for 2026 and beyond. We are not just building software; we are engineering the foundational layers of the autonomous economy. In this role, you will design scalable, resilient, and future-proof infrastructure that powers the next generation of generative AI and quantum-ready applications.
You will work at the intersection of hardware and software, collaborating with top-tier engineers to optimize our global compute clusters. If you are passionate about pushing the boundaries of what is possible in tech and want to leave a legacy in the 2026 era, we want to hear from you.
Responsibilities
- Architect Future-Proof Systems: Design and implement distributed cloud infrastructure capable of supporting the exponential growth of AI workloads by 2026.
- Optimize Compute Performance: Lead initiatives to optimize large-scale machine learning training pipelines and inference services.
- Infrastructure Migration: Spearhead the migration to next-gen Kubernetes and serverless architectures, ensuring zero-downtime transitions.
- Security & Compliance: Enforce rigorous security protocols across all AI infrastructure layers to protect sensitive data and proprietary models.
- Cross-Functional Leadership: Mentor a team of DevOps and Site Reliability Engineers, fostering a culture of continuous improvement and innovation.
- Scalability Strategy: Develop strategies for auto-scaling resources based on real-time demand and predictive analytics.
Qualifications
- Experience: 8+ years of experience in software engineering, with at least 4 years in a senior infrastructure or architecture role.
- Technologies: Deep expertise in Python, Go, or Java, and proficiency with containerization (Docker/Kubernetes) and orchestration.
- Cloud Mastery: Proven track record architecting on major cloud platforms (AWS, GCP, or Azure) with a focus on AI/ML services.
- AI Knowledge: Strong understanding of machine learning lifecycle, data pipelines, and model serving infrastructure.
- Problem Solving: Demonstrated ability to troubleshoot complex, high-stakes systems and resolve critical performance bottlenecks.
- Education: Bachelor’s degree in Computer Science, Engineering, or a related field (Master’s preferred).