Solutions Architect, Enterprise
About the role
Scale enterprise role leading technical execution for enterprise AI deals from discovery to deal close, with post-sale engagement to support account growth. Owns assigned enterprise accounts, conducts technical discovery, evaluates feasibility, builds prototypes, and serves as main technical contact for customer engineering and leadership teams.
Responsibilities
- Technical discovery and qualification: Lead technical discovery and complete the Technical Qualification Checklist, evaluating feasibility, data readiness, integration requirements, security/compliance, evaluation readiness, and stakeholder alignment. Provide go/no-go recommendations for prospective deals.
- Evaluation and success criteria: Define performance metrics and evaluation criteria before proposal stage, including quality dimensions, baselines, evaluation datasets, and subject-matter expert access requirements.
- Customer workshops: Lead executive technical workshops for target use cases. Prepare pre-read materials and build working prototypes or live demos to validate proposed technical architectures.
- Statement of Work (SOW) input: Supply the technical input package for SOW creation (architectural approach, prototype findings, risks, dependencies, and assumptions) and review draft SOWs for technical accuracy.
- Post-sale account engagement: Maintain long-term technical engagement with signed accounts, identify expansion opportunities for new use cases, and share delivery feedback with GTM teams.
- Vertical technical assets: Develop reference architectures, demo environments, discovery templates, and evaluation frameworks for your assigned industry vertical.
Qualifications
- Engineering background with customer-facing experience in solutions architecture, sales engineering, forward-deployed engineering, or technical consulting. Hands-on proficiency in Python or equivalent language.
- Experience managing technical discovery and evaluation for enterprise sales cycles, including security reviews and technical qualification.
- Practical knowledge of Generative AI and LLM architecture in production environments, including system design and reliability considerations.
- Ability to design evaluation methodologies and metrics for complex technical requirements.
- Effective communication skills across both technical staff and executive stakeholders.
- Clear technical communication and problem-solving skills when addressing unknown requirements.
- Ability to rapidly build proof-of-concept prototypes for customer requirements.
- Domain expertise in financial services, healthcare/life sciences, or consumer verticals.
Skills
Benefits
- comprehensive health, dental and vision coverage
- retirement benefits
- learning and development stipend
- generous PTO
- commuter stipend (optional)
About Scale AI
Data-centric infrastructure to accelerate the development of AI
Scale accelerates the development of AI within organizations of any size to deliver critical business insights and operational efficiency. Its data-centric infrastructure platform leverages RLHF (Reinforced Learning with Human Feedback) to help organizations build the strongest AI models that supercharge their business, with customers across industries including Meta, Microsoft, U.S. Army, DoD’s Defense Innovation Unit, Open AI, General Motors, Toyota Research Institute, Brex, Instacart and Flexport.
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