Backend Software Engineer (AI Squad)

You will

• Design, build, and operate backend services and APIs that power AI-driven, ML-driven, or automation-heavy product capabilities.

• Translate predictive logic and AI outputs into reliable backend behaviors that can be consumed by user-facing product flows.

• Build the service layer that allows intelligent features to be integrated into real workflows with strong standards on latency, reliability, and security.

• Ensure features are designed for production, not just experimentation, with clear ownership of deployment, monitoring, and maintainability.

You will

• Partner closely with the squad’s ML Engineers to productionize predictive models and LLM-driven capabilities.

• Integrate model-serving APIs or LLM calls into robust backend services (your squad, or the applicative squad’s services) with proper retries, fallbacks, and observability.

• Help define evaluation and monitoring patterns that make intelligent product behaviors measurable over time.

• Contribute to the engineering patterns that allow ML and AI capabilities to be reused across multiple product features.

You will

• Build backend capabilities that help automate repetitive tasks, anticipate user needs, or simplify complex workflows.

• Work on product experiences where AI or ML can reduce manual effort, improve decision quality, or shorten time to value for users.

• Partner with Product and Design to turn ambiguous ideas into concrete backend implementations with measurable impact.

• Bring pragmatism to delivery, balancing experimentation speed with long-term maintainability and trust.

You will

• Instrument services with logs, tracing, and metrics to support production visibility and continuous improvement.

• Define and uphold standards around latency, resilience, failure handling, and cost efficiency for AI-powered services.

• Build with responsible data handling, security, and privacy by default, especially when features interact with sensitive financial workflows.

• Embrace a “you build it, you run it” mindset, owning the health and quality of what you ship.

You will

• Work hand-in-hand with ML Engineers, Product Managers, and Designers to deliver AI-powered product capabilities end-to-end.

• Collaborate with applicative squads (or join them for a quarter) to integrate AI and ML services into existing user journeys and backend systems.

• Help define the technical interfaces and integration patterns that make intelligent services easier to adopt across the product.

• Share best practices in backend reliability, production readiness, and AI feature delivery across the engineering organization.

What success looks like in your first 90 days

• You’ve shipped or materially advanced a production-grade backend service powering an AI-driven or ML-driven product capability.

• You’ve partnered effectively with one or more of the squad’s ML Engineers to turn predictive or generative logic into a reliable user-facing backend flow.

• You’ve improved the production readiness of an intelligent feature, for example through better observability, service integration, fallback handling, or evaluation metrics.

• You’ve contributed to a reusable backend pattern that makes future AI-powered product features easier to build across Spendesk.

You have

• Significant experience on backend software engineering experience in production environments.

• A strong track record of designing and shipping reliable backend services with measurable user or business impact.

• Experience contributing to complex product initiatives in fast-paced, cross-functional teams.

• Exposure to ML-enabled or AI-enabled product features is a strong plus.

You have

• Strong backend engineering skills with TypeScript / Node.js or adjacent technologies.

• Experience designing APIs and service layers for complex product workflows.

• Good understanding of distributed systems, async processing, and operational reliability.

• Practical experience, or strong interest, in integrating predictive models, LLM APIs, or other AI capabilities into product backends.

• Familiarity with technologies such as Kafka, SQS, Step Functions, PostgreSQL, and modern observability practices.

Nice to have

• Experience productionizing ML-backed features such as classification, recommendation, forecasting, or automation

Experience in SaaS, fintech, or regulated environments

Not ticking every box? We’d still love to hear from you. At Spendesk, we value skills, potential and diverse experiences. If this role excites you and you believe you could contribute, we encourage you to apply