Role & Responsibilities
We are seeking a seasoned Solution Architect to design and lead AI infrastructure and private cloud initiatives. This role focuses on building scalable, high-performance environments to support AI/ML workloads, data platforms, and enterprise applications. The ideal candidate will have deep expertise in private cloud architectures, GPU-based computing, and modern data center technologies, along with the ability to align infrastructure strategy with business and AI innovation goals.
Key Responsibilities-
- Architect and design AI-ready infrastructure platforms, including GPU clusters, high-performance computing (HPC), and storage systems
- Define and implement private cloud solutions using technologies such as OpenStack and VMware
- Design scalable environments for AI/ML workloads, including training and inference pipelines
- Collaborate with data scientists, platform engineers, and infrastructure teams to translate AI requirements into infrastructure solutions
- Drive infrastructure modernization initiatives, including containerization and orchestration using Kubernetes
- Ensure high availability, performance, scalability, and security of AI platforms
- Design storage solutions optimized for AI workloads (e.g., distributed file systems, object storage)
- Implement networking architectures for high-throughput, low-latency data transfer
- Define automation strategies using Infrastructure as Code (IaC) and configuration management tools
- Establish governance, standards, and best practices for AI infrastructure and private cloud environments
- Evaluate emerging technologies and recommend solutions aligned with enterprise strategy
- Provide technical leadership and guidance across architecture, design, and implementation phases
Ideal Candidate
- Strong Solution Architect – AI Infrastructure & Private Cloud profiles
- Mandatory (Experience 1) – Must have 8+years of experience in IT infrastructure, cloud, or data center architecture roles
- Mandatory (Experience 2) – Must have strong expertise in private cloud and virtualization (OpenStack, VMware vSphere) along with solid knowledge of Linux, networking, and storage architectures.
- Mandatory (Experience 3) – Must have hands-on experience designing AI/ML infrastructure, including GPU-based systems (e.g., NVIDIA platforms), HPC, and AI-optimized storage
- Mandatory (Experience 4) – Must have strong experience with containerization and orchestration (Docker, Kubernetes) and IaC/automation tools (Terraform, Ansible)
- Mandatory (Experience 5) – Must have experience designing scalable AI/ML environments for training/inference pipelines, with high-throughput, low-latency networking and distributed storage
- Mandatory (Experience 6) – Must have familiarity with hybrid cloud integration (AWS, Azure, or GCP) and proven ability to lead architecture design with strong stakeholder management.
- Mandatory (Skill) – Must have familiarity with hybrid cloud integration involving AWS, Azure, or GCP.
- Preferred (Skill 1) – Certifications in cloud (AWS/Azure/GCP), Kubernetes, or VMware/OpenStack, along with experience in MLOps platforms and AI lifecycle management
- Preferred (Skill 2) – Knowledge of high-performance networking (InfiniBand, RDMA) and exposure to data lake architectures and big data platforms
- Preferred (Skill 3) – Experience in large-scale enterprise or hyperscale environments.
Shubham Vishwakarma
Full Stack Developer - Averlon
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Follow CutshortShubham Vishwakarma
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