Solution Architect – AI Infrastructure & Private Cloud

Solution Architect – AI Infrastructure & Private Cloud 📍 Locations: Bengaluru, Karnataka / Pune, Maharashtra 💼 Experience: 8 – 12 Years 💰 CTC: ₹19 – ₹25 LPA 🏢 Company: Startup / Small Enterprise 🗓 Working Days: 5 days/week 🚫 Notice Period: Max 30 days ✈️ Outstation Candidates: Allowed 💰Role Overview 📍Email: [Upgrade to PRO to see contact] We are hiring a Solution Architect – AI Infrastructure & Private Cloud to design and lead next-generation infrastructure for AI/ML workloads. This role focuses on building GPU-powered, scalable, and high-performance environments, aligning infrastructure strategy with AI innovation and enterprise needs. 💰Key Responsibilities Architect AI-ready infrastructure platforms including GPU clusters, HPC systems, and storage solutions Design and implement private cloud environments using: OpenStack VMware vSphere Build scalable infrastructure for AI/ML training & inference pipelines Collaborate with data scientists, platform engineers, and infra teams to translate AI needs into solutions Drive modernization using: Docker Kubernetes Ensure high availability, performance, scalability, and security Design AI-optimized storage systems (distributed file systems, object storage) Build low-latency, high-throughput networking architectures Define automation strategies using: Terraform Ansible Establish governance, standards, and best practices Evaluate emerging technologies and guide strategic adoption Provide architectural leadership across design and delivery phases 💰Mandatory Requirements 8+ years in IT infrastructure / cloud / data center architecture Strong expertise in: Private cloud & virtualization (OpenStack, VMware) Linux, networking, and storage architectures Hands-on experience with AI/ML infrastructure, including: GPU-based systems (e.g., NVIDIA platforms) HPC environments AI-optimized storage Strong experience in: Containers: Docker, Kubernetes IaC/Automation: Terraform, Ansible Proven experience designing: Scalable AI/ML environments (training & inference) High-throughput, low-latency systems Experience with hybrid cloud platforms: Amazon Web Services Microsoft Azure Google Cloud Platform Strong stakeholder management and architecture leadership 💰Preferred Qualifications Certifications: AWS / Azure / GCP, Kubernetes, VMware / OpenStack Experience in MLOps platforms & AI lifecycle management Knowledge of high-performance networking (InfiniBand, RDMA) Exposure to data lake / big data architectures Experience in large-scale enterprise or hyperscale environments Ideal Candidate Profile A hands-on Solution Architect who understands both: Deep infrastructure (GPU, networking, storage, private cloud) AI/ML workload requirements (training, inference, pipelines) Someone who can bridge AI teams + infrastructure teams + business strategy.