Software Engineer, Forward Deployed AI Solutions

What matters most

• Own technical discovery with customers

  The engineer works directly with enterprise customers and end users to understand workflows, systems, data quality, constraints, and adoption blockers, then turns findings into system and non-functional requirements. The AI Solutions Strategist owns business discovery and rollout planning.

• Design secure, operable integrations

  The role calls for system-context and data-flow diagrams, integration plans across Ramp and customer systems, and security models covering permissions, access patterns, and auditability. Operational plans must address monitoring, alerting, incidents, and runbooks.

• Validate AI workflows before production

  The engineer prototypes with end users and defines quality metrics, acceptance tests, and red-teaming. The posting also calls for ML/GenAI deployment judgment and experience with LLM systems, including RAG, agents, monitoring, and evaluations.

• Carry deployments through handoff and reuse

  Ownership extends from technical discovery through implementation, launch, and operational handoff. Deployed workflows should be reliable, measurable, supportable, and adopted; the engineer also turns deployments into reusable patterns, components, and playbooks.

Likely challenges to think through

• Balancing a fast prototype with production controls

  Likely preparation hypothesis: proving a workflow with end users may be faster than establishing its security, reliability, evaluation, and support model. Prepare to explain how you would stage validation and set acceptance criteria before launch.

• Reconciling customer-specific needs with reuse

  Likely preparation hypothesis: enterprise integrations and data quality vary, while the role favors existing Ramp capabilities and reusable components. Prepare an approach for deciding what to configure, build specifically, or generalize without assuming every deployment is alike.

• Making AI quality and adoption observable

  Likely preparation hypothesis: a workflow can pass a technical test yet fail on edge cases or see little sustained use. Prepare to connect end-user validation, quality metrics, red-teaming, monitoring, and operational handoff to evidence of production value.

Topics to prepare

• Technical discovery and requirements

  Exercise: For a hypothetical enterprise workflow, list questions about existing systems, data quality, constraints, and adoption blockers; then distinguish functional requirements from security, privacy, reliability, performance, scalability, and cost requirements. The posting assigns technical discovery and requirements to the engineer.

• ML and GenAI solution trade-offs

  Exercise: Break a hypothetical customer task into steps and explain when retrieval-augmented generation (RAG), an agent, or a simpler workflow would be appropriate, including deployment trade-offs. The role explicitly calls for ML/GenAI fundamentals and experience building RAG and agent systems.

• LLM evaluation and red-teaming

  Exercise: Define quality metrics, acceptance criteria, and adversarial cases for that hypothetical workflow; explain what would block launch. Evaluation plans, acceptance tests, red-teaming, and LLM evals are explicit in the posting.

• Secure, scalable integration architecture

  Exercise: Reason through data flows between a customer system and an AI workflow, including permissions, access patterns, auditability, failure handling, and scale. The posting requires secure system design, APIs, integrations, data pipelines, cloud architecture, and distributed systems patterns.

• Production readiness and adoption

  Exercise: Specify what to monitor after launch, what should trigger an alert, how an incident would be handled, and how end users would validate the workflow. The engineer owns production readiness and must deliver workflows that are supportable, measurable, and adopted.

• Finance operations context

  Exercise: Sketch the basic steps and likely data handoffs in an accounts-payable or expense workflow. This is optional preparation: the posting describes familiarity with finance operations as nice to have, not a requirement.

Skills to revise

• Conduct a mock technical discovery

  Practice a short customer interview, then produce a prioritized requirements brief with open questions and assumptions clearly marked. This rehearses the posting’s direct work with enterprise customers and end users from discovery onward.

• Document a solution architecture

  Draw a system-context diagram and data-flow diagram for a hypothetical integration, then add a concise integration plan and security model. These are artifacts the role explicitly creates and maintains; the scenario is only a practice suggestion.

• Build a small integrated LLM prototype

  In one language named in the posting, such as Python or TypeScript, implement a small workflow that reads sample data through an API-style interface and demonstrates retrieval or an agent step. Explain what existing product capability you would seek to reuse before custom-building; no specific Ramp primitive is named in the posting.

• Execute an evaluation plan

  Create sample cases, run the prototype against them, record quality results and failure examples, and test at least one adversarial case. This practices the posting’s hands-on prototyping, validation, evals, and red-teaming.

• Prepare an operational handoff

  Write a brief monitoring and alerting plan, an incident-response runbook, and a launch acceptance checklist for the prototype. The posting explicitly calls for these operational artifacts and a production handoff.

• Validate with end users and capture reuse

  Role-play a prototype review with an end user, record a change prompted by feedback, and extract one reusable component or playbook entry. The role calls for end-user validation and turning deployments into reusable patterns.

• Select a production project walkthrough

  Prepare a concise account of a real project you worked on, if available: customer discovery, your technical decisions, implementation, launch, handoff, and measured results. Identify your ownership and any lessons from production use.

• Practice technical discovery

  Draft questions for an enterprise customer about workflow goals, existing systems, data quality, constraints, and adoption blockers. Turn a sample set of answers into requirements for security, privacy, reliability, performance, scalability, and cost.

• Create a sample solution design

  As a preparation exercise, choose a plausible customer AI workflow and draw system-context and data-flow diagrams. Add an integration plan and a security model covering permissions, access patterns, and auditability; label assumptions about Ramp capabilities rather than inventing them.

• Prepare LLM implementation evidence

  Identify code or a project excerpt you can discuss in your strongest relevant language. Be ready to explain RAG or agent design choices, API and data-pipeline integration, cloud and distributed-system trade-offs, and how a prototype would be validated with end users.

• Write an evaluation plan

  For the sample workflow, define quality metrics, acceptance tests, and red-team cases. Explain how results would inform deployment decisions and how you would monitor quality after launch.

• Plan production handoff and reuse

  Outline monitoring, alerting, incident response, and a short runbook for the sample design. Note how you would check reliability and adoption, then extract a reusable component or playbook from the deployment.

• Bring targeted questions for Ramp

  Ask how engineers and AI Solutions Strategists divide decisions in practice, which Ramp product capabilities are commonly reused, and what production handoff entails. Clarify how the stated travel expectation of up to approximately 50% varies by project.