• Design, build, and maintain production-grade LLM integration pipelines — including retrieval-augmented generation (RAG), prompt engineering, output parsing, and chain orchestration. • Develop and operate AI features within Jeeves's core financial products: spend categorization, document extraction, anomaly detection, financial Q&A, and automated reconciliation. • Implement structured output validation, fallback handling, and confidence scoring to ensure AI decisions meet reliability standards for financial use cases. • Evaluate and integrate AI frameworks and tools (LangChain, LlamaIndex, OpenAI API, Anthropic API, HuggingFace, vector databases) and advocate for the right tool for the job. • Establish prompt versioning and evaluation practices to ensure AI outputs remain accurate and consistent as models and data evolve. • Design and maintain vector search pipelines using databases such as Pinecone, Weaviate, or pgvector to power semantic search and RAG-based features. • Build document ingestion and chunking pipelines for Jeeves's financial data — processing invoices, receipts, policy documents, and transaction records. • Optimize retrieval quality through embedding model selection, chunk strategy, metadata filtering, and re-ranking techniques. • Collaborate with data scientists to take trained ML models from experimental notebooks to production serving infrastructure. • Build and maintain model serving endpoints with appropriate latency SLOs, input validation, and output monitoring. • Implement model performance monitoring and data drift detection to ensure production models remain accurate over time. • Support model retraining workflows by designing clean data pipelines and feature engineering that can be continuously updated. • Integrate AI services cleanly with Jeeves's backend microservices — designing clear API contracts, circuit breakers, and graceful degradation patterns. • Write high-quality, testable backend code in Python or Go/Node.js to power AI-integrated features. • Instrument AI components with structured logging, distributed tracing, latency dashboards, and alerting to ensure operational visibility. • Build human-in-the-loop review workflows for AI decisions that require oversight — particularly for high-value financial actions. • Partner with Product, Backend Engineering, and Data Science to define the AI roadmap and translate requirements into reliable systems. • Contribute to a culture of quality by writing design docs, reviewing peers' AI system designs, and sharing learnings openly. • Help grow the AI engineering practice at Jeeves by establishing patterns, tooling, and best practices that the broader team can build on.

• Design, build, and maintain production-grade LLM integration pipelines — including retrieval-augmented generation (RAG), prompt engineering, output parsing, and chain orchestration. • Develop and operate AI features within Jeeves's core financial products: spend categorization, document extraction, anomaly detection, financial Q&A, and automated reconciliation. • Implement structured output validation, fallback handling, and confidence scoring to ensure AI decisions meet reliability standards for financial use cases. • Evaluate and integrate AI frameworks and tools (LangChain, LlamaIndex, OpenAI API, Anthropic API, HuggingFace, vector databases) and advocate for the right tool for the job. • Establish prompt versioning and evaluation practices to ensure AI outputs remain accurate and consistent as models and data evolve. • Design and maintain vector search pipelines using databases such as Pinecone, Weaviate, or pgvector to power semantic search and RAG-based features. • Build document ingestion and chunking pipelines for Jeeves's financial data — processing invoices, receipts, policy documents, and transaction records. • Optimize retrieval quality through embedding model selection, chunk strategy, metadata filtering, and re-ranking techniques. • Collaborate with data scientists to take trained ML models from experimental notebooks to production serving infrastructure. • Build and maintain model serving endpoints with appropriate latency SLOs, input validation, and output monitoring. • Implement model performance monitoring and data drift detection to ensure production models remain accurate over time. • Support model retraining workflows by designing clean data pipelines and feature engineering that can be continuously updated. • Integrate AI services cleanly with Jeeves's backend microservices — designing clear API contracts, circuit breakers, and graceful degradation patterns. • Write high-quality, testable backend code in Python or Go/Node.js to power AI-integrated features. • Instrument AI components with structured logging, distributed tracing, latency dashboards, and alerting to ensure operational visibility. • Build human-in-the-loop review workflows for AI decisions that require oversight — particularly for high-value financial actions. • Partner with Product, Backend Engineering, and Data Science to define the AI roadmap and translate requirements into reliable systems. • Contribute to a culture of quality by writing design docs, reviewing peers' AI system designs, and sharing learnings openly. • Help grow the AI engineering practice at Jeeves by establishing patterns, tooling, and best practices that the broader team can build on.

• Design, build, and maintain production-grade LLM integration pipelines — including retrieval-augmented generation (RAG), prompt engineering, output parsing, and chain orchestration. • Develop and operate AI features within Jeeves's core financial products: spend categorization, document extraction, anomaly detection, financial Q&A, and automated reconciliation. • Implement structured output validation, fallback handling, and confidence scoring to ensure AI decisions meet reliability standards for financial use cases. • Evaluate and integrate AI frameworks and tools (LangChain, LlamaIndex, OpenAI API, Anthropic API, HuggingFace, vector databases) and advocate for the right tool for the job. • Establish prompt versioning and evaluation practices to ensure AI outputs remain accurate and consistent as models and data evolve. • Design and maintain vector search pipelines using databases such as Pinecone, Weaviate, or pgvector to power semantic search and RAG-based features. • Build document ingestion and chunking pipelines for Jeeves's financial data — processing invoices, receipts, policy documents, and transaction records. • Optimize retrieval quality through embedding model selection, chunk strategy, metadata filtering, and re-ranking techniques. • Collaborate with data scientists to take trained ML models from experimental notebooks to production serving infrastructure. • Build and maintain model serving endpoints with appropriate latency SLOs, input validation, and output monitoring. • Implement model performance monitoring and data drift detection to ensure production models remain accurate over time. • Support model retraining workflows by designing clean data pipelines and feature engineering that can be continuously updated. • Integrate AI services cleanly with Jeeves's backend microservices — designing clear API contracts, circuit breakers, and graceful degradation patterns. • Write high-quality, testable backend code in Python or Go/Node.js to power AI-integrated features. • Instrument AI components with structured logging, distributed tracing, latency dashboards, and alerting to ensure operational visibility. • Build human-in-the-loop review workflows for AI decisions that require oversight — particularly for high-value financial actions. • Partner with Product, Backend Engineering, and Data Science to define the AI roadmap and translate requirements into reliable systems. • Contribute to a culture of quality by writing design docs, reviewing peers' AI system designs, and sharing learnings openly. • Help grow the AI engineering practice at Jeeves by establishing patterns, tooling, and best practices that the broader team can build on.

• Design, build, and maintain production-grade LLM integration pipelines — including retrieval-augmented generation (RAG), prompt engineering, output parsing, and chain orchestration. • Develop and operate AI features within Jeeves's core financial products: spend categorization, document extraction, anomaly detection, financial Q&A, and automated reconciliation. • Implement structured output validation, fallback handling, and confidence scoring to ensure AI decisions meet reliability standards for financial use cases. • Evaluate and integrate AI frameworks and tools (LangChain, LlamaIndex, OpenAI API, Anthropic API, HuggingFace, vector databases) and advocate for the right tool for the job. • Establish prompt versioning and evaluation practices to ensure AI outputs remain accurate and consistent as models and data evolve. • Design and maintain vector search pipelines using databases such as Pinecone, Weaviate, or pgvector to power semantic search and RAG-based features. • Build document ingestion and chunking pipelines for Jeeves's financial data — processing invoices, receipts, policy documents, and transaction records. • Optimize retrieval quality through embedding model selection, chunk strategy, metadata filtering, and re-ranking techniques. • Collaborate with data scientists to take trained ML models from experimental notebooks to production serving infrastructure. • Build and maintain model serving endpoints with appropriate latency SLOs, input validation, and output monitoring. • Implement model performance monitoring and data drift detection to ensure production models remain accurate over time. • Support model retraining workflows by designing clean data pipelines and feature engineering that can be continuously updated. • Integrate AI services cleanly with Jeeves's backend microservices — designing clear API contracts, circuit breakers, and graceful degradation patterns. • Write high-quality, testable backend code in Python or Go/Node.js to power AI-integrated features. • Instrument AI components with structured logging, distributed tracing, latency dashboards, and alerting to ensure operational visibility. • Build human-in-the-loop review workflows for AI decisions that require oversight — particularly for high-value financial actions. • Partner with Product, Backend Engineering, and Data Science to define the AI roadmap and translate requirements into reliable systems.