Backend Software Engineer (AI Squad) in London

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 hands‑on backend role focuses on turning predictive models, LLM capabilities, and business intelligence into real product experiences.

You will build backend services, APIs and MCPs that bring automation, prediction, and assisted decision‑making into Spendesk’s user journeys, reducing manual work and making our product more proactive and intelligent. You will collaborate closely with the 3 ML Engineers, Product Managers, Designers and other squads across Spendesk to 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, you’ll partner with ML Engineers to production‑ize predictive logic, expose it through clean APIs and services, and integrate it into workflows that automate tasks, simplify decision‑making, or anticipate user needs.

Features You May Work On

• Automated categorization and enrichment of spend‑related workflows
• Predictive assistance in finance or accounting journeys
• Intelligent recommendations based on historical behaviour or contextual signals
• LLM‑powered experiences that simplify user actions and reduce friction
• Backend services that make AI capabilities reusable across multiple product flows

Key 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 behaviours 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.

Productionisation of ML and LLM capabilities

• Partner closely with the squad’s ML Engineers to production‑ize predictive models and LLM‑driven capabilities.
• Integrate model‑serving APIs or LLM calls into robust backend services with proper retries, fallbacks, and observability.
• Help define evaluation and monitoring patterns that make intelligent product behaviours measurable over time.
• Contribute to engineering patterns that allow ML and AI capabilities to be reused across multiple product features.

Automation, prediction & workflow simplification

• 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, observability & operational ownership

• 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.

Cross‑functional collaboration

• 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 we’re looking for

Experience & background

• Significant experience in backend software engineering 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.

Technical & data skills

• 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.

Leadership & collaboration

• 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.

Nice To Have

• Experience productionising 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.

Location and ways of working

We value regular in‑person collaboration. We’re primarily hiring in Paris, London or Barcelona with a flexible hybrid setup. Outstanding remote candidates may be considered, but this is not a remote‑first role.

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, e.g. 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.

Benefits

• Flexible on‑site and remote policy
• Latest Apple equipment — the tools you need to excel
• Access to Moka.care — for emotional and mental health wellbeing
• Great office snacks — to fuel your day
• A positive team to work with daily!
• Location‑specific benefits tailored to each market, including health insurance, wellness allowances, commuter support, meal vouchers, and gym memberships — ensuring you’re well supported wherever you’re based.

Diversity & Inclusion

At Spendesk, we’re committed to fostering an environment where all differences are encouraged, supported and celebrated. We’re building our culture for everyone, with everyone. Our goal is to attract and build a diverse, equal and inclusive team, where everyone feels welcome and we truly embrace and encourage people from all backgrounds to apply.