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-powered features for spend categorization, document extraction, anomaly detection, financial Q&A, and automated reconciliation.
  • Implement structured output validation, fallback handling, and confidence scoring for financial AI decisions.
  • Evaluate and integrate AI frameworks and tools such as LangChain, LlamaIndex, OpenAI, Anthropic, HuggingFace, and vector databases.
  • Establish prompt versioning and evaluation practices to keep AI outputs accurate and consistent over time.
  • Design and maintain vector search pipelines to support semantic search and RAG-based features.
  • Build document ingestion and chunking pipelines for invoices, receipts, policy documents, and transaction records.
  • Collaborate with data scientists to move ML models from notebooks into production serving infrastructure.
  • Build and maintain model-serving endpoints with latency SLOs, input validation, and output monitoring.
  • Integrate AI services with backend microservices, add observability, and create human-in-the-loop review workflows for high-value actions.

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, with familiarity in at least one 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, 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 system health.
  • Preferred experience in fintech, financial services, or another regulated industry where reliability and auditability matter.
  • Preferred familiarity with prompt evaluation frameworks, A/B testing AI outputs, and tracking model performance degradation in production.
  • Preferred experience with ML lifecycle tools such as MLflow, Weights & Biases, Vertex AI, or SageMaker.
  • Preferred knowledge of real-time data streaming tools such as Kafka or Kinesis.
  • Preferred open-source contributions, technical writing, or conference talks in AI/ML.
  • Preferred startup or scale-up experience building foundational systems in ambiguous environments.

Interested in this position?

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