Staff Software Engineer, AI Platform

At Ripple, we’re building a world where value moves like information does today. It’s big, it’s bold, and we’re already doing it. Through our crypto solutions for financial institutions, businesses, governments and developers, we are improving the global financial system and creating greater economic fairness and opportunity for more people, in more places around the world. And we get to do the best work of our career and grow our skills surrounded by colleagues who have our backs.

If you’re ready to see your impact and unlock incredible career growth opportunities, join us, and build real world value.

The Opportunity

The mission, should you choose to accept it, is to pioneer and scale GSmart, Ripple Treasury's AI platform, into the future. If you enjoy solving complex business problems in a fast-paced, collaborative, and iterative environment this might be the right role for you!

We are seeking an innovative and forward-thinking Staff Software Engineer passionate about building a world-class AI platform through a world-class team. You will set the technical direction for how AI is built, measured, and operated across our enterprise treasury products, and stay hands-on in design, implementation, and production.

About the AI Platform Team

The AI Platform team builds and operates GSmart, the production AI platform behind the intelligent features in Ripple Treasury. Enterprise treasury teams in 160+ countries rely on it for forecasting, risk, onboarding, and analyst experiences.

GSmart is middleware, not a single feature. It gives every product team model routing, guardrails, observability, evaluation, and context injection out of the box, so each new AI capability is cheaper and safer to build than the last.

We're now extending the platform into an agentic layer: agents that reason over treasury data and act on behalf of finance teams, with the auditability that regulated financial workflows demand. We hold a high bar on measurement. If we can't evaluate it, we don't ship it.

What You'll Do – Be a Force Multiplier

Technical Leadership & Architecture:

• Drive architectural decisions for the AI platform, balancing scalability, maintainability, cost, and rapid delivery

• Establish the standards and design patterns for how teams build on GSmart: context assembly, prompt and model management, guardrails, and agent orchestration

• Lead technical design reviews and give constructive feedback that elevates the team's architectural thinking

• Identify and retire technical debt strategically, with roadmaps that improve both developer velocity and platform reliability

Evaluation & AI Quality:

• Own the evaluation strategy for the platform: datasets, rubrics, automated and human review, and regression gates in CI/CD

• Make AI quality measurable, so every prompt, model, or context change ships with evidence rather than opinion

• Diagnose failure modes in production LLM systems and turn them into evals that prevent recurrence

Team Enablement & Mentorship:

• Mentor mid-level and senior engineers, helping them grow into technical leadership roles

• Break down complex, ambiguous problems into actionable work that distributes 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

Strategic Technical Vision:

• Anticipate scalability, latency, and cost challenges before they become blockers (e.g., 10x growth in inference volume or expansion into new data-residency regions)

• Evaluate emerging models, frameworks, and agent patterns, and recommend when and how to adopt them

• Balance technical excellence with pragmatic delivery, knowing when "good enough now" beats "perfect later"

Ownership & Accountability:

• Own critical platform systems end-to-end, from design through production support

• Take responsibility for production reliability and proactively prevent incidents

• Step into ambiguous situations and drive them to resolution without needing explicit direction

• Lead by example in code quality, documentation, and operational excellence

• Make tough technical trade-off decisions and clearly communicate the reasoning

What You'll Work On

• AI middleware — the shared services every AI feature runs through: model routing across providers, guardrails, memory, and context injection

• Evaluation and observability — tracing, evals, prompt registry, experimentation, and cost tracking that make AI behavior visible and auditable

• Agentic layer — the orchestration, tooling, and safety model for agents that work across treasury data and workflows

• Regional deployment — infrastructure-as-code that brings inference and memory into new geographies while meeting data-sovereignty requirements

• Product integration — partnering with Forecasting, Risk, Liquidity, and Digital Assets teams to turn customer problems into platform capabilities

Essential Knowledge / Experience

Years & Scope:

• 8+ years of professional software engineering experience

• 3+ years working on complex, business-critical systems in production

• 2+ years building and operating LLM-based systems in production, beyond proof of concept

• Strong communication skills with experience working in cross-functional projects

• Track record of delivering projects that span multiple quarters and require coordination across teams

Technical Leadership:

• Led technical designs that were implemented by teams of 3+ engineers

• Made architectural decisions that had lasting impact on system capabilities

• Established technical patterns that were adopted across teams

AI Platform Engineering:

• Has owned a production LLM platform or shared AI service used by multiple product teams

• Can define and run evaluations: golden datasets, rubric and model-graded scoring, regression testing, and online monitoring

• Understands context engineering and retrieval, and can explain when each is the right tool

• Hands-on with model gateways and routing, prompt management, guardrails, and LLM observability (we use Azure OpenAI, LiteLLM, and Langfuse)

• Experience designing agentic systems, including tool use, orchestration, memory, and failure handling

• Speaks fluently about failure modes, cost, and latency trade-offs, not just wins

Cloud & Infrastructure:

• Proficiency with at least one major cloud platform (preferably Azure)

• Experience with containerized workloads (Docker; Azure Container Apps or Kubernetes)

• Understanding of infrastructure as code (Terraform) and CI/CD pipeline design

• Knowledge of observability: logging, metrics, tracing, and alerting strategies

Core Technologies:

• Strong full-stack skills , front-end through back-end and APIs, in 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 knowing when to use alternative patterns (streaming, gRPC, webhooks)

• Working knowledge of data stores used in AI systems, including relational databases and caches such as Redis

Nice to Have:

• Experience in fintech, treasury, banking, or another regulated domain

• Experience with data-residency or multi-region AI deployments

• Familiarity with digital assets

What You Will Get

• A high impact, high visibility role at a growing SaaS company that values personal growth, accountability, and the concept of “good work”

• This is a great opportunity for someone who wants to make a big impact, work in a fast-paced and collaborative environment, and win as a team to scale a growing business

• A culture of open collaboration and problem solving

• An empowered role on the Engineering team, responsible for the platform every AI feature in Ripple Treasury is built on

• Great benefits, and culture

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