Solutions Architect, Enterprise
Solutions Engineer at Scale AI designs and delivers tailored GenAI demos and pilots for enterprise clients, scoping technical requirements and securing technical wins through close collaboration with sales, product, and engineering teams.
- Compensation
- $180K–$225K / yr
- Location
- San Francisco, CA; New York, NY
- Work setup
- Not specified
- Seniority
- Lead
- Employment
- Full time
- Experience
- Not specified
- Category
- Architecture
- Posted
Skills
Must have
- Engineering background with customer-facing experience in solutions architecture, sales engineering, forward-deployed engineering, or technical consulting. Hands-on proficiency in Python or equivalent
- 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.
Nice to have
- Experience with domain compliance requirements: model risk management and fair lending (FSI), HIPAA and clinical safety (HCLS), or high-volume quality measurement (Consumer).
- Background in forward-deployed engineering or shipping production ML systems.
- Experience creating large-scale evaluation datasets alongside domain experts.
- Experience building or scaling a solutions engineering practice or playbook.
Full description
The role
Scale builds enterprise AI systems deployed into production. As a Solutions Architect, you lead technical execution for enterprise deals in your vertical from initial discovery through deal close, and remain involved post-sale to support account growth.
You own an assigned set of enterprise accounts. You conduct technical discovery, evaluate technical feasibility, build solution prototypes, and serve as the main technical point of contact for customer engineering and leadership teams.
You work alongside three primary partners: the Account Executive (commercial owner), the Project Manager (delivery owner), and the Tech Lead (engineering lead for implementation).
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What you will own
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 clear 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.
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What we are looking for
- 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.