Senior AI Engineer

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Jeeves

SiteLinkedInTodas as Vagas

51 - 200 funcionários

💼 Consultoria

📦 Logística

📣 Marketing

Consulting • Logistics • Marketing

Jeeves é uma plataforma financeira que oferece soluções simplificadas para a gestão de finanças globais. Ela permite que as empresas emitam cartões, enviem pagamentos e gerenciem despesas em uma plataforma tudo em um, disponível em mais de 25 países ao redor do mundo. Ao integrar diversos serviços financeiros, a Jeeves aumenta a eficiência e reduz custos para empresas tradicionalmente sobrecarregadas por sistemas financeiros fragmentados. Com ofertas como transferências internacionais de fundos, taxas de câmbio (FX) competitivas, gestão de despesas e integrações diretas com a contabilidade, a Jeeves amplia as capacidades operacionais em múltiplas moedas e regiões. Embora não seja um banco, a empresa faz parcerias com bancos e instituições financeiras licenciadas para oferecer esses serviços.

Descrição

• 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.

🎯 Requisitos

• 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 in a production environment. • Experience designing and operating RAG pipelines, including chunking strategies, embedding models, and vector database integration (Pinecone, Weaviate, pgvector, or similar). • Strong proficiency in Python for AI/ML workloads; familiarity with at least one AI orchestration framework (LangChain, LlamaIndex, or equivalent). • Experience with ML model serving infrastructure: REST or gRPC inference endpoints, input/output validation, latency budgeting, and monitoring. • Solid backend engineering fundamentals: REST APIs, relational databases (PostgreSQL preferred), async patterns, and cloud infrastructure (AWS, GCP, or Azure). • Experience with observability tooling: structured logging, distributed tracing, and building dashboards for AI system health. • **Preferred Qualifications** • Experience in fintech, financial services, or any regulated industry where AI reliability and auditability are critical. • Familiarity with prompt evaluation frameworks, A/B testing AI outputs, and tracking model performance degradation in production. • Experience with ML lifecycle management tools: MLflow, Weights & Biases, Vertex AI, or SageMaker. • Knowledge of real-time data streaming (Kafka, Kinesis) for event-driven AI pipelines. • Contributions to open-source AI tooling, published technical writing, or talks at AI/ML conferences. • Prior startup or scale-up experience — comfortable with ambiguity and building foundational systems from scratch.