Job Description
We are looking for a visionary Lead AI Infrastructure Architect to define the technical roadmap for 2026 and beyond. At Nexus Horizon Tech, we are building the operating system for the next generation of autonomous agents and generative intelligence. You will be responsible for architecting scalable, secure, and high-performance infrastructure that supports our cutting-edge AI models.
In this role, you will bridge the gap between deep learning research and production-grade engineering. You will lead a team of engineers to deploy and manage massive-scale machine learning workloads, ensuring reliability and performance in a rapidly evolving technological landscape.
Why Join Us?
We offer competitive compensation, remote-first flexibility, and the opportunity to work on projects that define the future of technology.
Responsibilities
- Architect and deploy resilient AI infrastructure on hybrid cloud platforms (AWS/GCP/Azure) optimized for 2026 workloads.
- Lead the integration of Generative AI models and Large Language Models (LLMs) into scalable production environments.
- Design and implement edge computing solutions to reduce latency in real-time AI applications.
- Oversee data pipelines, ensuring high availability, security, and compliance with global data regulations.
- Collaborate with cross-functional teams to translate business requirements into technical roadmaps.
- Drive technical innovation in quantum-readiness and sustainable computing practices.
Qualifications
- 10+ years of experience in DevOps, Site Reliability Engineering, or Cloud Architecture with a focus on AI/ML.
- Deep expertise in containerization technologies (Docker, Kubernetes) and orchestration.
- Strong proficiency in Python, TensorFlow, PyTorch, or similar machine learning frameworks.
- Experience with MLOps tools and CI/CD pipelines for automated model training and deployment.
- Excellent problem-solving skills and the ability to mentor senior engineering teams.
- Master's degree in Computer Science, Engineering, or a related field is preferred.