AI Engineer

Jeeves

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

Posted 10/10/2026full-timeRemote • ArgentinaSeniorLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and operating LLM-powered systems, managing finance workflows, and integrating AI services with backend systems. Proficient in designing agentic workflows and ensuring compliance through effective logging and monitoring.

Highest-signal resume keywords

LLM System DevelopmentPython ProgrammingAPI IntegrationAI Workflow DesignObservability for AI Systems

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Software EngineeringLLM ApplicationsREST APIsPostgreSQLAsync PatternsCloud Services (AWS, GCP, Azure)Error HandlingEvaluation MetricsVector DatabasesLogging and Tracing

Soft Skills

Customer EngagementProblem DefinitionEffective Communication

Tools & Technologies

Anthropic APIOpenAI APIPgvectorPineconeWeaviateClaude CodeCursorCodex

Industry Keywords

Financial Product ComplianceAI Engineering PatternsHuman-in-the-Loop SystemsRAG Systems

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformPostgresPython

About the role

Key responsibilities & impact

Own finance 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, with 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
Engage directly with customers and internal finance users to understand 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 Jeeves's backend using API contracts, retries, graceful degradation, and per-customer data isolation
Manage model cost and latency
Build evaluation sets and automated evaluations; detect regressions when prompts, models, or data change
Instrument AI components with logging, tracing, dashboards, and alerts
Maintain an audit trail of AI decisions for a regulated financial product
Establish shared AI engineering patterns, tooling, and practices
Demonstrate effective use of coding agents and AI tools to the wider team
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
A 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 (tool use, orchestration, human review steps), or RAG systems with vector databases such as pgvector, Pinecone, or Weaviate
Strong proficiency in Python, plus solid backend fundamentals: REST APIs, PostgreSQL or similar relational databases, async patterns, and a major cloud provider (AWS, GCP, or Azure)
Uses AI coding agents and tools (for example Claude Code, Cursor, or Codex) as a regular part of how they build, and can explain where they help and where they don't
Experience with observability for AI systems: logging, tracing, dashboards, and quality monitoring
Professional fluency in English, written and spoken