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Ripple

Enterprise blockchain payments and CBDC solutions

Staff Software Engineer - AI Platform

AI Application Engineering +1Updated on Nov 13
Full-timeChicago, IL, USA

About the Job

Requirements

  • At least 8 years of professional software engineering experience.
  • At least 3 years working on complex, business-critical systems in production.
  • At least 2 years building and operating LLM-based systems in production, beyond proof of concept.
  • Experience delivering multi-quarter projects requiring coordination across teams.
  • Experience leading technical designs implemented by teams of at least 3 engineers.
  • Experience making architectural decisions with lasting impact on system capabilities and establishing technical patterns adopted across teams.
  • Experience owning a production LLM platform or shared AI service used by multiple product teams.
  • Ability to define and run evaluations using golden datasets, rubrics, model-graded scoring, regression testing, and online monitoring.
  • Understanding of context engineering and retrieval, including when each is appropriate.
  • Hands-on experience with model gateways and routing, prompt management, guardrails, and LLM observability; the posting names Azure OpenAI, LiteLLM, and Langfuse.
  • Experience designing agentic systems, including tool use, orchestration, memory, and failure handling.
  • Ability to discuss failure modes, cost, and latency trade-offs.
  • Proficiency with at least one major cloud platform, preferably Azure.
  • Experience with containerized workloads such as Docker, Azure Container Apps, or Kubernetes.
  • Understanding of infrastructure as code, including Terraform, and CI/CD pipeline design.
  • Knowledge of observability practices, including logging, metrics, tracing, and alerting strategies.
  • Strong full-stack skills spanning front end, back end, and APIs, using a modern stack such as C#/.NET and TypeScript/Node.js.
  • Deep experience building large-scale, multi-tenant enterprise applications.
  • Experience building and maintaining RESTful APIs and choosing when to use alternatives such as streaming, gRPC, and webhooks.
  • Working knowledge of data stores used in AI systems, including relational databases and caches such as Redis.
  • Strong communication skills and experience working on cross-functional projects.

Responsibilities

  • Drive architectural decisions for the AI platform, balancing scalability, maintainability, cost, and rapid delivery.
  • Establish standards and design patterns for teams building on GSmart, including context assembly, prompt and model management, guardrails, and agent orchestration.
  • Lead technical design reviews and provide constructive feedback to improve the team’s architectural thinking.
  • Identify and strategically retire technical debt through roadmaps that improve developer velocity and platform reliability.
  • Own the platform evaluation strategy, including datasets, rubrics, automated and human review, and regression gates in CI/CD.
  • Make AI quality measurable so prompt, model, or context changes are supported by evidence before release.
  • Diagnose production LLM system failure modes and turn them into evaluations that prevent recurrence.
  • Mentor mid-level and senior engineers as they develop technical leadership skills.
  • Break complex, ambiguous problems into actionable work distributed effectively across the team.
  • Enable product teams to build on the platform and own their deliverables through clear interfaces, documentation, and reference implementations.
  • Champion engineering practices including observability, incident response, and production excellence.
  • Anticipate scalability, latency, and cost challenges, including inference-volume growth and expansion into new data-residency regions.
  • Evaluate emerging models, frameworks, and agent patterns and recommend when and how to adopt them.
  • Balance technical quality with pragmatic delivery decisions.
  • Own critical platform systems end-to-end, from design through production support.
  • Take responsibility for production reliability and proactively prevent incidents.
  • Resolve ambiguous situations without requiring explicit direction.
  • Lead by example in code quality, documentation, and operational excellence.
  • Make technical trade-off decisions and communicate the reasoning clearly.
  • Build shared AI middleware services, including model routing across providers, guardrails, memory, and context injection.
  • Build evaluation and observability capabilities including tracing, evaluations, prompt registry, experimentation, and cost tracking.
  • Develop the agentic layer, including orchestration, tooling, and safety for agents working across treasury data and workflows.
  • Build infrastructure as code for regional deployment of inference and memory while meeting data-sovereignty requirements.
  • Partner with Forecasting, Risk, Liquidity, and Digital Assets teams to translate customer problems into platform capabilities.

Desired Qualifications

  • Experience in fintech, treasury, banking, or another regulated domain.
  • Experience with data residency or multi-region AI deployments.
  • Familiarity with digital assets.

About the Company

Ripple

Enterprise blockchain payments and CBDC solutions

What Ripple does: Ripple provides enterprise blockchain-enabled financial services that speed up payments and improve cash management. How it works: its platform, built on blockchain and cryptocurrency, enables real-time settlement, liquidity management, working-capital access, and instant payments for financial institutions, enterprises, and governments, including the ability to source crypto assets and manage treasury via a single platform. How it differs: it focuses on scalable, secure CBDC implementations and government partnerships (e.g., Palau) to deliver central-bank-grade digital currencies, alongside proven faster remittances and lower costs from clients like Nium and Tranglo. Its goal: help clients move money faster, more transparently, and at lower cost, while expanding access to digital currencies and CBDCs.

Company size
1,001-5,000
Company stage
Debt Financing
Total funding
$1.1B
Headquarters
San Francisco, CA
Founded
2012

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