AI Engineer

Jeeves

. Own finance AI workflows end to end, from problem definition through production and ongoing improvement .

Posted 10/10/2026full-timeRemote • BrazilSeniorLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and operating LLM-powered systems, with a focus on end-to-end workflow ownership, integration of AI services, and maintaining compliance in regulated financial environments. Proficient in designing and implementing AI workflows, including retrieval-augmented generation (RAG) systems and observability practices.

Highest-signal resume keywords

LLM System DevelopmentPython ProgrammingAI Workflow DesignAPI IntegrationObservability for AI Systems

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Applicant Tracking System Keywords

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Hard Skills

Software EngineeringLLM ApplicationsREST APIsPostgreSQLVector DatabasesAI Quality MonitoringError HandlingStructured OutputsLoggingTracing

Soft Skills

Problem DefinitionCustomer CollaborationTeam Training

Tools & Technologies

AWSGCPAzureClaude CodeCursorCodexPgvectorPineconeWeaviate

Industry Keywords

Financial AI WorkflowsHuman-in-the-LoopAI Decision Audit TrailAI Engineering Patterns

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformPostgresPython

About the role

Key responsibilities & impact

Own finance AI workflows end to end, from problem definition through production and ongoing improvement
Design agentic and LLM-based systems combining extraction, retrieval, reasoning, and tool use
Define human-in-the-loop points for high-value financial decisions
Write design documents, make build-vs-buy and model choices, and set workflow success metrics
Work directly with customers and internal finance users to identify workflow problems and define completion criteria
Build production-grade LLM pipelines with prompt and context design, structured outputs, validation, fallbacks, and confidence scoring
Design retrieval and RAG components including chunking, embeddings, vector search, and re-ranking
Integrate AI services with backend systems using API contracts, retries, graceful degradation, and per-customer data isolation
Manage model cost and latency
Build evaluation sets and automated evaluations, and catch regressions
Implement logging, tracing, dashboards, alerts, and AI quality monitoring
Maintain an audit trail of AI decisions for a regulated financial product
Establish shared AI engineering patterns, tooling, and practices
Train the wider team on effective use of coding agents and AI tools
Review AI system designs and share learnings

Requirements

What you’ll need

7+ years of professional software engineering experience, including at least 2 years building and operating LLM or AI-powered systems in production
Track record of owning a large system or workflow end to end, from scoping and design through launch and iteration, with limited direction
Hands-on experience shipping LLM-powered applications with APIs such as Anthropic, OpenAI, or similar, including structured outputs, error handling, and evaluation
Experience designing agentic or multi-step AI workflows, or RAG systems with vector databases such as pgvector, Pinecone, or Weaviate
Strong proficiency in Python
Solid backend fundamentals: REST APIs, PostgreSQL or similar relational databases, async patterns, and a major cloud provider such as AWS, GCP, or Azure
Regular use of AI coding agents and tools such as Claude Code, Cursor, or Codex
Experience with observability for AI systems: logging, tracing, dashboards, and quality monitoring
Professional fluency in English, written and spoken

Benefits

Comp & perks

Remote work or hybrid work based out of São Paulo
Flexible schedule for coming into the office at complexo JK Iguatemi
Opportunity to work at a stablecoin-native banking platform serving global enterprises
Steep learning curve and hands-on experience with production AI systems