Solutions Architect

Xaxis Solutions; United States; On-site; USD 150,000 - 200,000; Full time

Job summary

Xaxis Solutions is seeking a Senior Enterprise Architect to design and ship production-scale systems across complex environments.

You’ll lead cloud-native architectures on Azure/AWS/GCP, with containers, serverless, data services, and robust security and governance. You’ll read and write system design docs, mentor teams, and collaborate with product, engineering, and executives across time zones in the US and Canada.

Qualifications

• 10+ years in solution or enterprise architecture.
• designing and shipping production systems at scale.
• read and write a system design doc; reason about distributed systems and performance.
• AI/ML solution design with real-time inference and MLOps lifecycle.
• security, data privacy, governance in regulated environments.

Responsibilities

• Design and ship production-scale systems in complex environments.
• Own cloud-native architectures across Azure/AWS/GCP.
• Collaborate with product, engineering, and executives across time zones.
• Mentor teams on architecture best practices.
• Ensure security, governance, and compliance across platforms.

Skills

Cloud architecture

Python proficiency

AI/ML solution design

Regulatory compliance

Stakeholder influence

Tools

Docker

Kubernetes

Cloud platforms (Azure/AWS/GCP)

Job description

10+ years in solution or enterprise architecture, with a track record of designing and shipping production

systems at scale in complex, high-stakes organizations.

• Cloud-native depth. Hands-on expertise architecting on a major cloud (Azure, AWS, or GCP) — containers and orchestration (Docker, Kubernetes), serverless, API management, messaging, identity, and data services.
• Technical fluency and hands-on ability. You can read and write a system design doc, reason about distributed systems and performance, and stay dangerous in Python day to day.
• AI/ML solution design. Experience architecting ML, Generative AI, and RAG systems — real-time inference, multi-agent orchestration, vector databases, and the MLOps lifecycle (training, versioning, deployment, monitoring, CI/CD).
• Enterprise-grade rigor. Comfort with security, data privacy, governance, and audit-ready design in regulated, compliance-sensitive environments.
• Demonstrated ownership of a platform or solution with meaningful revenue, adoption, or usage — with specifics you can walk us through.
• Exceptional written communication and the ability to influence without direct authority across product, engineering, and executive stakeholders.
• Based anywhere in the US or Canada and able to collaborate across time zones; occasional travel for