Senior Backend Engineer, Jeeves

• 5+ years of professional backend engineering experience building and operating production systems
• Fluent in English — professional fluency required for daily work with global teams in written and spoken contexts
• Strong proficiency in at least one backend language: Go, Python, or Node.js/TypeScript
• Experience designing and building RESTful APIs, microservices, and event-driven backend systems
• Solid understanding of relational databases (PostgreSQL preferred): schema design, query optimization, and data modeling
• Experience with cloud infrastructure (AWS, GCP, or Azure), containerization (Docker, Kubernetes), and CI/CD pipelines
• Demonstrated ability to integrate third-party APIs reliably in production — including error handling, retry logic, and observability
• Experience working on globally distributed teams across time zones and regions
• Comfortable using AI tools as part of everyday engineering work — integrating LLM API outputs into backend services and using AI coding assistants fluently

Desirable

• Jeeves is looking for a Senior Backend Engineer to join our engineering team. You will design and build the backend systems that power Jeeves's financial platform — working across payments, cards, spend management, and compliance infrastructure that serves businesses across the Americas and beyond
• This is a backend engineering role at its core, we're looking for a strong backend engineer who knows how to work effectively with AI tools, understands where AI can accelerate development and product capabilities, and is comfortable integrating AI-powered features into production backend systems
• Design, build, and maintain scalable, reliable backend services that process financial transactions and serve Jeeves customers across 20+ countries
• Write clean, testable, production-quality code in Go, Python, or Node.js/TypeScript; participate actively in design and code reviews
• Build and consume RESTful and GraphQL APIs; design inter-service communication using gRPC, message queues, and event-driven architectures
• Design and optimize relational and non-relational database schemas (PostgreSQL, MongoDB, Redis) for correctness, performance, and scale
• Own backend features end-to-end — from scoping and technical design through deployment, monitoring, and iteration
• Implement security best practices: authentication, authorization, input validation, and data protection across distributed services
• Integrate LLM API calls (e.g., OpenAI, Anthropic) into backend services as product features — such as spend categorization, document parsing, or natural language workflows — ensuring those integrations are reliable, observable, and cost-efficient
• Build backend pipelines that consume AI-generated outputs safely: validate structured outputs, handle fallback scenarios, and design graceful degradation when AI services are unavailable or return low-confidence results
• Collaborate with AI and data science teams to integrate model outputs into backend APIs — bridging experimental AI work and production systems
• Use AI coding tools (GitHub Copilot, Claude, Cursor, etc.) fluently as part of your everyday development workflow
• Instrument services with structured logging, distributed tracing, and metrics for full operational visibility
• Participate in on-call rotation; respond to production incidents and contribute to post-incident reviews
• Contribute to CI/CD pipeline improvements, testing infrastructure, and deployment practices
• Work closely with engineering, product, compliance, and data teams across multiple time zones and regions — communicating in both English and Spanish or Portuguese as the situation requires
• Contribute to a globally distributed engineering culture through thorough documentation, async design reviews, and thoughtful pull request feedback
• Bring your regional perspective to product and engineering conversations — our LATAM customers have specific needs, and engineers who understand those markets make our product better