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Ramp is building intelligent financial infrastructure for enterprise companies, automating over $200B in annualized spend across 70,000+ organizations. The company handles high-stakes, data-dense problems: payment authorization, risk flagging, spend categorization, and financial close automation.
As a Software Engineer on the AI Solutions team, you will be a deeply client-facing technical leader, co-owning customer engagements alongside an AI Solutions Strategist. While the Strategist drives business discovery, ROI narrative, and stakeholder alignment, you own the technical side: discovery, solution design, prototyping, implementation, and production readiness. You will spend significant time with enterprise customers, moving projects from initial bootcamp and workflow discovery through implementation, launch, and steady production usage.
Key responsibilities include:
- Translating customer goals into clear system and non-functional requirements (security, privacy, reliability, performance, scalability, cost)
- Partnering directly with customers to understand workflows, constraints, data quality, and adoption blockers
- Creating solution architecture artifacts: system context diagrams, data flow diagrams, integration plans, security models, evaluation plans, and operational runbooks
- Leveraging Ramp's core primitives to build efficiently and reuse existing product capabilities
- Prototyping and validating workflows with end users to de-risk approaches and prove product-market fit
- Driving projects end-to-end from bootcamp through implementation, production launch, and operational handoff
- Ensuring deployed workflows are reliable, supportable, measurable, and adopted by customer teams
- Converting deployments into reusable patterns, components, and playbooks for future projects
You should have production software shipping experience in high-ownership environments, with proven ability to work directly with enterprise customers from discovery through implementation. Solutions architecture, technical consulting, forward-deployed engineering, or pre-sales engineering experience is expected. Strong fundamentals in ML/GenAI (problem decomposition, evaluation, deployment trade-offs) and hands-on coding ability in Python, TypeScript/JavaScript, Java, Go, or similar are required. You must be comfortable designing secure, scalable systems and producing clear technical documentation. Experience with cloud architecture (AWS, GCP, Azure), distributed systems, and LLM systems (RAG, agents, monitoring, evals) is essential. Familiarity with finance operations workflows (AP, procurement, expenses, close, reconciliation) is a nice-to-have. The role requires willingness to travel up to ~75% as needed, flexible based on project and client needs.
