Enterprise Sales Engineer: Regulated Finance AI

Obin AI; New York (NY); Hybrid; USD 150,000 - 210,000; Full time

Job summary

Obin AI is seeking its first sales engineer to partner with Client Partners and Account Executives across the full cycle from discovery to handoff into delivery. You will translate client problems into engineering scope and translate our build into a business buyer's language.

As the sole SE, you will own technical discovery, the business-demo flow, architecture reviews, and model-risk questions, building an enablement library for the team.

Qualifications

• Enterprise sales engineering experience with seven-figure deals.
• Financial services fluency including security review and risk blocks.
• Ability to demo to a business buyer, not only engineers.
• Deep understanding of LLMs, RAG, agentic workflows, and practical limits.
• Builder mindset: comfortable without a playbook or team.
• Has previously killed a deal on technical grounds and explained why.

Responsibilities

• Lead technical discovery translating client workflows into engineering scope.
• Own business-buyer demos with context storytelling.
• Drive architecture and security reviews focusing on data access and auditability.
• Address model risk and compliance questions for regulated clients.
• Explain production accuracy from frontier models to deployed systems.
• Translate opportunities into estimates and scope for SOW.
• Ensure clean handoff to delivery and enablement for the team.

Skills

Enterprise SE experience

Financial services fluency

Business-ready demos

Applied AI understanding

Builder mindset

Deal critique justification

Job description

Obin AI is seeking its first sales engineer to partner with Client Partners and Account Executives across the full cycle from discovery to handoff into delivery. You will translate client problems into engineering scope and translate our build into a business buyer's language.

As the sole SE, you will own technical discovery, the business-demo flow, architecture reviews, and model-risk questions, building an enablement library for the team.