Solutions Architect

About This Opportunity

We are representing a confidential AI infrastructure company in a search for a Solutions Architect. In this role, you'll own the technical relationship across the entire customer lifecycle—from pre-sale discovery through post-sale implementation and optimization. You'll serve as the technical partner behind strategic enterprise accounts, working alongside sales leadership to architect solutions that integrate deeply into customers' ML pipelines and data workflows.

This is a customer-facing technical role requiring genuine hands-on experience with data labeling, ML infrastructure, and enterprise SaaS deployment. You'll work directly with Data Scientists, ML Engineers, and MLOps teams to design solutions that scale in production environments.

Responsibilities

• Partner with Account Executives on strategic deals, leading technical discovery, tailored demos, architecture reviews, and proof-of-concepts against real customer data and workflows
• Drive technical onboarding, guiding customers through installation, secure configuration, and best-practice deployment across cloud, on-prem, or hybrid environments
• Architect integrations between the platform and customer AI/ML workflows, pipelines, storage, and enterprise systems
• Build custom solutions including scripts, plug-ins, and API integrations to extend the platform for unique customer requirements
• Serve as the senior technical escalation point for your accounts, resolving advanced issues and collaborating with Product, Engineering, and Support teams
• Deliver enablement sessions, workshops, and documentation that make customer teams self-sufficient on the platform
• Act as a trusted technical advisor to customer engineering, data, and AI/ML teams, supporting adoption, expansion, and long-term value

What We're Looking For

• 5+ years in a customer-facing technical role such as Solutions Architect, Sales Engineer, or Professional Services Engineer for a highly technical product, ideally enterprise SaaS or ML/AI platforms; experience on both sides of the sale is ideal
• Direct hands-on experience building data labeling pipelines—whether data annotation, labeling workflows, or the broader ML lifecycle; you can explain how it works to an engineer and to a VP without changing much
• Practical experience integrating SaaS platforms with ML pipelines; you've deployed models to production or done the engineering to prepare datasets for data science teams
• Fluency in Python, REST APIs, and infrastructure tools including Linux/Unix and Docker; Kubernetes is a plus; you've built against multiple client APIs and SDKs; JavaScript, CSS, and HTML are beneficial
• Proven track record running technical discovery, demos, and proof-of-concepts during the sales cycle, then owning onboarding, implementation, troubleshooting, and custom development after the close
• Business acumen to accompany technical depth—you can translate a customer's problem into a value proposition they recognize and anticipate objections before they surface
• Executive presence and strong writing skills, with the range to communicate effectively with Data Scientists, ML Engineers, and technical executives
• Comfortable managing multiple complex accounts and competing priorities simultaneously

How We Work With You

Candidates apply directly through Get A Job.ai. Our talent team conducts an initial screening to understand your background and confirm alignment with the role requirements. Qualified candidates are then submitted to our client for consideration. Please do not attempt to contact the client directly, as all communication is managed through our recruiting process.

Pay

Pay via Get A Job.ai has not been specified for this role. Compensation details will be discussed during the screening process.

Equal Opportunity: Get A Job.ai is committed to inclusive hiring practices and works with clients who value diverse perspectives and backgrounds.