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 chain orchestration.
  • Develop AI features for core financial products such as spend categorization, document extraction, anomaly detection, financial Q&A, and automated reconciliation.
  • Implement structured output validation, fallback handling, and confidence scoring for financial AI use cases.
  • Evaluate and integrate AI frameworks and tools, and choose the most appropriate tools for each use case.
  • Establish prompt versioning and evaluation practices to keep AI outputs accurate and consistent over time.
  • Design and maintain vector search pipelines and document ingestion workflows for financial documents and transaction records.
  • Optimize retrieval quality through embedding selection, chunking strategy, metadata filtering, and re-ranking techniques.
  • Collaborate with data scientists to productionize ML models and build serving infrastructure 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, and build human-in-the-loop review workflows for sensitive decisions.

Requirements

  • Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
  • 5+ years of professional software engineering experience, including 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 experience with REST APIs, PostgreSQL or other relational databases, async patterns, and cloud infrastructure such as AWS, GCP, or Azure.
  • 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.
  • Preferred: familiarity with prompt evaluation frameworks, A/B testing AI outputs, and tracking production model degradation.
  • Preferred: experience with ML lifecycle management tools such as MLflow, Weights & Biases, Vertex AI, or SageMaker.
  • Preferred: knowledge of real-time data streaming tools such as Kafka or Kinesis.
  • Preferred: contributions to open-source AI tooling, technical writing, or conference talks in AI/ML.
  • Preferred: prior startup or scale-up experience and comfort building foundational systems in ambiguity.

Benefits

  • Full-time remote position.
  • Opportunity to work on AI systems embedded in real financial products used by businesses globally.
  • High ownership role shaping how Jeeves builds with AI as the company scales.
  • Work alongside backend engineers, data scientists, and product teams on production systems.
  • Backed by well-known investors and a well-funded company environment.
  • Exposure to a fast-growing, cross-functional engineering organization.
  • Chance to contribute to AI engineering standards, tooling, and best practices at the company.

Interested in this position?

Apply directly on the company website

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