Backend Software Engineer

Build AI-powered product capabilities that automate, predict, and simplify user workflows

Spendesk is looking for a Backend Software Engineer (IC3) to join our AI & Data Products squad and help build the next generation of product features powered by AI, ML, and intelligent automation.

This is a hands‑on backend role focused on turning predictive models, LLM capabilities, and business intelligence into real product experiences. You will work on backend services, APIs and MCPs that bring automation, prediction, and assisted decision‑making into Spendesk’s user journeys, helping reduce manual work and make our product more proactive and intelligent.

You will collaborate closely with the 3 ML Engineers in the squad, as well as Product Managers, Designers, and applicative squads across Spendesk. Together, you will build production‑grade services that expose ML‑driven and LLM‑driven capabilities in ways that are reliable, observable, and valuable for end users.

About The Role

As a Backend Software Engineer (IC3) in the AI & Data Products squad, you will design, build, and operate backend services that power AI‑native and ML‑native product features.

Your mission is to help Spendesk move from isolated intelligence components to real, user‑facing product capabilities. In practice, this means partnering with ML Engineers to productionize predictive logic, expose it through clean APIs and services, and integrate it into workflows that automate tasks, simplify decision‑making, or anticipate user needs.

You May Work On Features Such As

• automated categorization and enrichment of spend‑related workflows
• predictive assistance in finance or accounting journeys
• intelligent recommendations based on historical behavior or contextual signals
• LLM‑powered experiences that simplify user actions and reduce friction
• backend services that make AI capabilities reusable across multiple product flows

This role requires both strong backend engineering and genuine interest in AI‑powered product design. You won’t be expected to build the models yourself, but you will be responsible for making them usable, scalable, secure, and effective in production.

Our tech environment

You’ll operate in a modern engineering environment designed for both product delivery and AI integration:

• TypeScript
• Node.js for backend and banking applications
• React on the frontend
• PostgreSQL for data storage; Redis, SQS, and Kafka for jobs, queues, and event streaming
• Terraform to define infrastructure as code
• Kubernetes, Lambdas, and Step Functions to run our applications
• AWS as our cloud provider, including AWS Bedrock for LLM access
• GitHub Actions for CI

You do not need to be an ML engineer, but you must be comfortable integrating predictive or generative capabilities into backend systems and user‑facing product workflows.

Key responsibilities

Backend services for AI and ML‑powered product features

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.

Productionization of ML and LLM capabilities

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.

Automation, prediction & workflow simplification

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.

Reliability,