Design & Lead: Drive end-to-end AI infrastructure architecture across hardware, software, networking, and multi-cloud environments.
Innovate & Implement: Select and deploy best-in-class tools (TensorFlow, PyTorch, Kubernetes, Docker) for scalable AI platforms.
Optimize & Secure: Ensure high performance, cost-efficiency, and compliance with security, data protection, and ethical AI standards.
Build Data Pipelines: Architect efficient data ingestion, storage, processing, and management systems for AI/ML models.
Mentor & Inspire: Provide technical leadership and guidance to cross-functional teams.
Troubleshoot & Improve: Resolve infrastructure issues to maintain availability and reliability.
Stay Current: Track advancements in AI, ML, and cloud to drive innovation.
Document: Maintain clear, comprehensive infrastructure documentation.

What You’ll Bring

Education: Bachelor’s/Master’s in Computer Science, Engineering, or related field.
Experience:
- 15+ years in infrastructure architecture, including 3–5 years in AI-specific infrastructure.
- Proven track record in scalable, secure AI solutions in cloud environments.
- Hands-on with Docker, Kubernetes, GPU systems, NVIDIA tools, and deep learning architectures (LLMs).
Technical Skills: Python, cloud models (IaaS/PaaS/SaaS), IaC (Terraform/CloudFormation), networking, storage, security best practices, DevOps/MLOps.
Soft Skills: Problem-solving, strategic thinking, excellent communication, leadership, adaptability.

Mandatory Skills: HPUX admin, Kubernetes, Nutanix, Solaris 10/11, Azure Infra, VMware ESXi, Windows Server, VMware VxRail HCI, AWS Infra, RedHat Linux, GCP Infra, AIX admin, RedHat OpenShift, Infrastructure Architecture, Dell EMC VxRail.