Software Engineer, AI Solutions

What You’ll Do

• Translate customer goals into clear system requirements and non-functional requirements covering security, privacy, reliability, performance, scalability, and cost.
• Partner directly with customers to understand current workflows, constraints, systems, data quality, and adoption blockers.
• Create and maintain solution architecture artifacts: system context and data flow diagrams, integration plan across Ramp and customer systems, security model covering permissions/access patterns/auditability, evaluation plan covering quality metrics/acceptance tests/red-teaming, operational plan covering monitoring/alerting/incident response/runbooks.
• Leverage core Ramp primitives to build efficiently, reusing existing product capabilities wherever possible.
• Prototype and validate workflows with end users to de-risk the approach and prove product-market fit.
• Drive projects from bootcamp and technical discovery through implementation, production launch, and operational handoff.
• Ensure deployed workflows are reliable, supportable, measurable, and adopted by customer teams.
• Convert deployments into reusable patterns, components, and playbooks for future AI Solutions projects.

What You Need

• Experience shipping production software in high-ownership environments.
• Ability to work directly with enterprise customers from discovery through production implementation.
• Experience in solutions architecture, technical consulting, forward deployed engineering, or pre-sales engineering.
• Strong fundamentals in ML/GenAI, including problem decomposition, evaluation, and deployment trade-offs.
• Strong coding ability in at least one of: Python, TypeScript/JavaScript, Java, Go, or similar.
• Ability to design secure, scalable systems and produce clear technical documentation.
• Comfort working across APIs, integrations, data pipelines, customer systems, and cloud infrastructure.
• Experience with cloud architecture on AWS, GCP, or Azure, and distributed systems patterns.
• Experience building LLM systems, including RAG, agents, monitoring, and evals.
• Willingness to travel up to ~75% as needed, flexible based on project needs and client needs.

Nice to Have

• Familiarity with finance operations workflows such as AP, procurement, expenses, close, reconciliation, and reporting.