Backend Software Engineer (AI Squad)

Spendesk is looking for a Backend Software Engineer to join their AI & Data Products squad and help build AI-powered product features. This role involves designing, building, and operating backend services that integrate predictive models and machine learning capabilities into user workflows.

Responsibilities:

• 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
• 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
• 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
• 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
• 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

Requirements:

• 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
• 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
• Highly autonomous and comfortable owning backend systems from design to production
• Product-minded, customer-focused, and motivated by building features that create visible value for end users
• Comfortable working closely with ML Engineers and translating their outputs into durable product capabilities
• Pragmatic and impact-driven, able to move from experimentation to production without losing engineering rigor
• Fluent in written and spoken English, our business language
• Experience productionizing ML-backed features such as classification, recommendation, forecasting, or automation
• Experience integrating LLM-backed capabilities into product workflows
• Familiarity with evaluation patterns for AI-powered features
• Experience in SaaS, fintech, or regulated environments