We're looking for a Senior AI Engineer who is obsessed with building AI systems that actually work in production: reliable, observable, cost-efficient, and genuinely useful. This is not a research role. You will ship AI-powered features that process real financial data for real businesses.
\nLLM & AI Pipeline Engineering - Design, build, and maintain production-grade LLM integration pipelines , including retrieval-augmented generation (RAG), prompt engineering, output parsing, and chain orchestration.
\nDevelop and operate AI features within Jeeves's core financial products: spend categorization, document extraction, anomaly detection, financial Q&A, and automated reconciliation.
\nImplement structured output validation, fallback handling, and confidence scoring to ensure AI decisions meet reliability standards for financial use cases.
\nEvaluate and integrate AI frameworks and tools (LangChain, LlamaIndex, OpenAI API, Anthropic API, HuggingFace, vector databases) and advocate for the right tool for the job.
\nEstablish prompt versioning and evaluation practices to ensure AI outputs remain accurate and consistent as models and data evolve.
\nRetrieval & Vector Search - Design and maintain vector search pipelines using databases such as Pinecone, Weaviate, or pgvector to power semantic search and RAG-based features.
\nBuild document ingestion and chunking pipelines for Jeeves's financial data , processing invoices, receipts, policy documents, and transaction records.
\nOptimize retrieval quality through embedding model selection, chunk strategy, metadata filtering, and re-ranking techniques.
\nML Model Serving & Operations - Collaborate with data scientists to take trained ML models from experimental notebooks to production serving infrastructure.
\nBuild and maintain model serving endpoints with appropriate latency SLOs, input validation, and output monitoring.
\nImplement model performance monitoring and data drift detection to ensure production models remain accurate over time.
\nSupport model retraining workflows by designing clean data pipelines and feature engineering that can be continuously updated.
\nBackend Integration & Reliability - Integrate AI services cleanly with Jeeves's backend microservices , designing clear API contracts, circuit breakers, and graceful degradation patterns.
\nWrite high-quality, testable backend code in Python or Go/Node.js to power AI-integrated features.
\nInstrument AI components with structured logging, distributed tracing, latency dashboards, and alerting to ensure operational visibility.
\nCollaboration & Growth - Partner with Product, Backend Engineering, and Data Science to define the AI roadmap and translate requirements into reliable systems.
\nContribute to a culture of quality by writing design docs, reviewing peers' AI system designs, and sharing learnings openly.
\nHelp grow the AI engineering practice at Jeeves by establishing patterns, tooling, and best practices that the broader team can build on.
\nXML job scraping automation by YubHub
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\nLLM & AI Pipeline Engineering - Design, build, and maintain production-grade LLM integration pipelines , including retrieval-augmented generation (RAG), prompt engineering, output parsing, and chain orchestration.
\nDevelop and operate AI features within Jeeves's core financial products: spend categorization, document extraction, anomaly detection, financial Q&A, and automated reconciliation.
\nImplement structured output validation, fallback handling, and confidence scoring to ensure AI decisions meet reliability standards for financial use cases.
\nEvaluate and integrate AI frameworks and tools (LangChain, LlamaIndex, OpenAI API, Anthropic API, HuggingFace, vector databases) and advocate for the right tool for the job.
\nEstablish prompt versioning and evaluation practices to ensure AI outputs remain accurate and consistent as models and data evolve.
\nRetrieval & Vector Search - Design and maintain vector search pipelines using databases such as Pinecone, Weaviate, or pgvector to power semantic search and RAG-based features.
\nBuild document ingestion and chunking pipelines for Jeeves's financial data , processing invoices, receipts, policy documents, and transaction records.
\nOptimize retrieval quality through embedding model selection, chunk strategy, metadata filtering, and re-ranking techniques.
\nML Model Serving & Operations - Collaborate with data scientists to take trained ML models from experimental notebooks to production serving infrastructure.
\nBuild and maintain model serving endpoints with appropriate latency SLOs, input validation, and output monitoring.
\nImplement model performance monitoring and data drift detection to ensure production models remain accurate over time.
\nSupport model retraining workflows by designing clean data pipelines and feature engineering that can be continuously updated.
\nBackend Integration & Reliability - Integrate AI services cleanly with Jeeves's backend microservices , designing clear API contracts, circuit breakers, and graceful degradation patterns.
\nWrite high-quality, testable backend code in Python or Go/Node.js to power AI-integrated features.
\nInstrument AI components with structured logging, distributed tracing, latency dashboards, and alerting to ensure operational visibility.
\nBuild human-in-the-loop review workflows for AI decisions that require oversight , particularly for high-value financial actions.
\nCollaboration & Growth - Partner with Product, Backend Engineering, and Data Science to define the AI roadmap and translate requirements into reliable systems.
\nContribute to a culture of quality by writing design docs, reviewing peers' AI system designs, and sharing learnings openly.
\nHelp grow the AI engineering practice at Jeeves by establishing patterns, tooling, and best practices that the broader team can build on.
\nXML job scraping automation by YubHub
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\nThis role sits within Aladdin Platform Engineering and focuses on the infrastructure and platform services required to support machine learning models, large language models (LLMs), and emerging AI capabilities in production. The successful candidate will work closely with AI Engineers, Data Scientists, Platform Engineers, Security, and Product partners to deliver resilient, cloud native AI platforms in a highly regulated environment.
\nKey Responsibilities
\nQualifications
\nOur Benefits
\nTo help you stay energized, engaged, and inspired, we offer a wide range of employee benefits including: retirement investment and tools designed to help you in building a sound financial future; access to education reimbursement; comprehensive resources to support your physical health and emotional well-being; family support programs; and Flexible Time Off (FTO) so you can relax, recharge, and be there for the people you care about.
\nOur Hybrid Work Model
\nBlackRock’s hybrid work model is designed to enable a culture of collaboration and apprenticeship that enriches the experience of our employees, while supporting flexibility for all. Employees are currently required to work at least 4 days in the office per week, with the flexibility to work from home 1 day a week. Some business groups may require more time in the office due to their roles and responsibilities. We remain focused on increasing the impactful moments that arise when we work together in person – aligned with our commitment to performance and innovation. As a new joiner, you can count on this hybrid model to accelerate your learning and onboarding experience here at BlackRock.
\nXML job scraping automation by YubHub
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