Senior Software Engineer, Spendesk

• Significant experience on backend software engineering experience in production environments
• A strong track record of designing and shipping scalable backend services with clear ownership of reliability and maintainability
• Experience contributing to complex technical projects in fast-paced product organizations
• Ideally, hands-on exposure to AI-enabled or LLM-backed product features in production
• Strong backend engineering skills with TypeScript / Node.js or adjacent technologies
• Good fluency in service design, asynchronous architectures, and resilient distributed systems
• Experience integrating APIs and external services into robust product backends
• Practical experience, or strong interest, in integrating LLMs into production systems, ideally using platforms such as AWS Bedrock, OpenAI, or Anthropic
• Familiarity with technologies such as Kafka, SQS, Step Functions, PostgreSQL, and modern observability practices
• Highly autonomous and comfortable owning ambiguous technical problems from framing to delivery
• Able to work effectively in partnership with a Staff Engineer while independently delivering complex components
• Product-minded, customer-focused, and capable of translating business needs into backend systems
• Curious about AI-native product design and motivated by building durable foundations, not just short-term demos
• Fluent in written and spoken English, our business language
• Experience integrating LLMs with tool-calling, guardrails, and evaluation loops
• 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

Desirable

• Spendesk is looking for a Senior Backend Software Engineer (IC4) to join our PDE (Product, Design & Engineering) organization and help shape the technical foundations of our next generation of AI-powered product experiences
• This is a senior, hands-on role for an engineer who combines strong backend fundamentals with solid fluency in LLM-enabled systems
• You will work on the technical foundations behind conversational and agentic experiences: backend services for context handling, orchestration, tool calling, and reliable interactions with LLMs and applicative systems
• You will partner closely with a Staff Engineer (IC5) on the same scope. Together, you will shape and implement the architecture that enables AI systems to interact safely and efficiently with Spendesk’s product ecosystem
• While the IC5 is expected to drive more transversal and system-wide architecture topics, you will be a key senior builder on these foundations, owning complex services end-to-end and helping turn architectural direction into production reality
• As a Senior Backend Software Engineer (IC4), you will help create the backend layer that supports agentic and conversational experiences for Spendesk end users
• Your scope will include services that:
• Manage conversations and contextual state,
• Support the ingestion and retrieval of rich content such as PDFs and images,
• Integrate LLMs through AWS Bedrock with robust operational patterns,
• Connect to MCP-compatible services and tools exposed by other squads,
• Orchestrate multi-step workflows combining reasoning, tool usage, and product actions
• This is not just about integrating services’ features to an LLM. You will build production-grade services that need to be reliable, observable, secure, and designed for long-term extensibility
• You will contribute to reusable patterns and collaborate with ML Engineers, Product Managers, Designers, and applicative squads to make these foundations useful across multiple AI use cases
• 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
• Design, build, and operate backend services and APIs that power conversational and agentic AI features
• Implement the services needed for context management, tool invocation, rich content access, and multi-step orchestration
• Build robust patterns for integrating LLMs through AWS Bedrock, including retries, fallbacks, tracing, and cost-aware usage
• Ensure these services are designed for production from day one, with strong standards on reliability, maintainability, and security
• Partner with the IC5 Staff Engineer to implement the architecture supporting AI-native product experiences
• Contribute to reusable service boundaries, contracts, and interfaces that can be adopted across squads
• Help define how applicative squads can expose MCP-compatible tools and capabilities to central AI services
• Translate high-level architectural direction into concrete technical implementations and scalable engineering patterns
• Own complex backend components end-to-end, from design and implementation to deployment and maintenance
• Contribute directly to proof-of-concepts and experiments, then harden the successful ones into reliable production services
• Review code, challenge design decisions, and raise the quality bar on backend engineering within the squad
• Bring pragmatism to delivery, balancing speed of experimentation with long-term platform quality
• Instrument services with logs, tracing, and metrics to ensure strong production visibility and fast iteration
• Help define and uphold quality standards around latency, failure handling, cost efficiency, and resilience
• Contribute to a strong “you build it, you run it” culture, where backend engineers own what they ship in production
• Ensure AI-related services are built with secure coding and responsible data handling practices
• Collaborate with Product Managers and Designers to turn AI product concepts into concrete backend capabilities
• Partner with applicative squads to connect backend services with the tools, context, and business actions needed by AI systems
• Help create shared engineering knowledge around LLM integration, async workflows, and reliable AI feature delivery
• What success looks like in your first 90 days
• You’ve shipped or materially advanced a production-grade backend component that supports conversational or agentic AI experiences at Spendesk
• You’ve partnered effectively with the IC5 Staff Engineer to turn architectural direction into reliable, reusable backend services
• You’ve integrated at least one LLM-backed capability through AWS Bedrock with appropriate observability, retries, and operational safeguards in place
• You’ve improved one shared engineering capability, for example orchestration patterns, service contracts, or cost/reliability monitoring, that can be reused by future AI features

• HR screening call
• Discussion with the Hiring Manager
• Technical interview, live coding and/or system design depending on profile
• Final interview with leadership
• As we are an international team, please submit your application and CV in English