Jeeves

Jeeves

Jeeves is a global financial infrastructure company that offers open business accounts, corporate cards, cross-border payments, and expense management through a borderless platform. With operations in 25+ countries, Jeeves simplifies and centralizes fi...

Diversified Financial Services
251-1K
$368M raised

Description

  • Design, build, and maintain production-grade LLM pipelines, including RAG, prompt engineering, output parsing, and orchestration.
  • Develop AI features for core financial products such as spend categorization, document extraction, anomaly detection, financial Q&A, and automated reconciliation.
  • Implement output validation, fallback handling, and confidence scoring to make AI decisions reliable for financial use cases.
  • Evaluate and integrate AI frameworks, APIs, and vector database tools, and choose the right tool for each problem.
  • Establish prompt versioning and evaluation practices to keep AI outputs accurate and consistent over time.
  • Design and maintain vector search and document ingestion pipelines for invoices, receipts, policy documents, and transaction records.
  • Collaborate with data scientists to productionize trained ML models and build serving endpoints with monitoring and latency controls.
  • Integrate AI services with backend microservices, including API contracts, circuit breakers, and graceful degradation patterns.
  • Instrument AI systems with logging, tracing, dashboards, and alerting to ensure operational visibility.
  • Partner with Product, Backend Engineering, and Data Science to define the AI roadmap and improve engineering practices.

Requirements

  • Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
  • 5+ years of professional software engineering experience, with at least 3 years focused on AI/ML systems in production.
  • Hands-on experience building and deploying LLM-powered applications using APIs such as OpenAI, Anthropic, or Cohere.
  • Experience designing and operating RAG pipelines, including chunking strategies, embedding models, and vector database integration.
  • Strong proficiency in Python for AI/ML workloads.
  • Familiarity with at least one AI orchestration framework such as LangChain or LlamaIndex.
  • Experience with ML model serving infrastructure, including REST or gRPC inference endpoints, validation, latency budgeting, and monitoring.
  • Solid backend engineering fundamentals, including REST APIs, relational databases, async patterns, and cloud infrastructure.
  • Experience with observability tooling such as structured logging, distributed tracing, and dashboards for AI system health.
  • Preferred experience in fintech, financial services, or another regulated industry where AI reliability and auditability matter.
  • Familiarity with prompt evaluation frameworks, A/B testing AI outputs, and tracking model performance degradation in production.
  • Experience with ML lifecycle management tools such as MLflow, Weights & Biases, Vertex AI, or SageMaker.
  • Knowledge of real-time data streaming tools such as Kafka or Kinesis.
  • Contributions to open-source AI tooling, published technical writing, or conference talks are preferred.
  • Prior startup or scale-up experience building foundational systems in ambiguous environments is preferred.

Benefits

  • Full-time remote position.
  • Opportunity to work on AI systems embedded in a global financial platform used by 5,000+ clients across 20+ countries.
  • Backed by top investors, including Andreessen Horowitz, Y Combinator, CRV, Tencent, and Stanford University.
  • Opportunity to build production AI systems with direct impact on real financial workflows.
  • Work alongside backend engineers, data scientists, and product teams in a growing AI engineering practice.

Interested in this position?

Apply directly on the company website

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