Solutions Architect
Role details
Job location
Tech stack
Job description
We're looking for an early-career engineer who wants to get their hands on real, complex infrastructure - on-prem AI compute and hybrid cloud across all three major Hyperscalers - in an R&D setting where experimentation is encouraged and learning is built into the work. You'll grow alongside experienced engineers and build toward a Senior Architect track over time.
If you run a home lab, have broken something on a weekend just to understand why, or get genuinely excited about new hardware and infrastructure tech - this role was written for you. Duties include:
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Support and maintain on-prem GPU cluster infrastructure used for AI training, inference, and R&D workloads.
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Work hands-on with all three major cloud hyperscalers (AWS, Azure, GCP), including AI/ML services and experimental deployments.
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Use AI developer tools (GitHub Copilot, Claude Code, Google Gemini) as part of your everyday workflow.
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Build, rack, cable, and configure servers, GPU hardware, network switches, and storage devices in a lab environment.
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Support POC and testing environments - standing things up, validating them, and tearing them down as needed.
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Monitor system performance, troubleshoot hardware and software issues, and perform routine maintenance.
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Assist with system administration: user management, software deployment, and basic network configuration.
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Participate in infrastructure automation efforts including Ansible playbook execution and containerization.
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Document lab processes and procedures, and infrastructure configurations.
Requirements
- Bachelor's degree in CS, IT, or related field - or equivalent experience that shows you know your stuff.
- Genuine curiosity about infrastructure, AI systems, and how things work at a low level.
- Strong troubleshooting instincts and a "I'll figure it out" mindset.
- Clear communicator - comfortable talking to both internal teammates and partners
- Hands-on experience with Windows and/or Linux/Unix - even on your own hardware.
- Networking fundamentals: TCP/IP, VLANs, subnetting.
- Scripting in Python, Bash, or PowerShell.
- GPU hardware familiarity or exposure to AI/ML compute infrastructure.
- Virtualization platforms: Hyper-V, Nutanix, Proxmox, & Red Hat OpenShift
- Physical infrastructure knowledge (not exhaustive): Lenovo, Dell, Cisco, HPE, NetApp & Everpure
- Infrastructure-as-code: Ansible, Docker, or Kubernetes.
Be AmbITious: This opportunity is not just about what you do today but also about where you can go tomorrow. When you bring your hunger, heart, and harmony to Insight, your potential will be met with continuous opportunities to upskill, earn promotions, and elevate your career.