Enterprise AI Solution Architect - Multi-Cloud & MLOps

Domino Data Lab; United States; On-site; USD 200,000 - 250,000; Full time

Benefits offered by this job

Equity

401(k) plan

Health benefits

Job summary

Domino Data Lab is seeking a Solutions Architect to lead technical execution for large-scale, multi-cloud deployments and integrate Domino into complex enterprise IT landscapes. You will design reference architectures spanning data, security, and infrastructure, and build field integrations across AI/ML ecosystems.

We value hands-on experience with Kubernetes in production, cloud platforms, and building solutions for regulated environments.

Qualifications

• 5+ years hands-on with Kibernetes in production (K8s) — upgrades, migrations, DR, scale.
• End-to-end enterprise K8s implementations in cloud or on-prem, OpenShift a strong signal.
• Direct experience owning strategic customer relationships at CTO/platform-lead level.
• Hands-on with at least one major cloud (AWS/Azure/GCP) and translate security/compliance to architectures.
• Hands-on AI/ML or data platform experience (Domino or similar MLOps) a strong plus.

Responsibilities

• Lead technical execution on Domino's most strategic deployments — multi-cloud, on-prem, and hybrid.
• Design end-to-end reference architectures in regulated environments (data, identity, security, infra).
• Build and document field integrations across AI/ML ecosystem and enable Solutions teams.
• Partner with account and SE teams on scoping, demos, and PoCs that map to real customer AI use cases.
• Translate field insights into roadmap input for Product and Engineering.

Skills

Kubernetes production

Cloud platforms AWS/Azure/GCP

Enterprise architecture

CTO-level engagement

MLOps / AI platforms

Tools

Terraform

OpenShift

Job description

Domino Data Lab is seeking a Solutions Architect to lead technical execution for large-scale, multi-cloud deployments and integrate Domino into complex enterprise IT landscapes. You will design reference architectures spanning data, security, and infrastructure, and build field integrations across AI/ML ecosystems.

We value hands-on experience with Kubernetes in production, cloud platforms, and building solutions for regulated environments.