Full Stack Engineer

it"). - Contribute to backend services and data layers : Extend existing .NET microservices and databases to expose the capabilities needed by AI tools. - Set up and improve CI/CD pipelines to ship reliably and frequently. - Apply DevOps and Operational Excellence best practices to ensure agent tools are observable, resilient, and secure. - Share knowledge with the team and help define how we build AI-powered products. In one year, you'll know you were successful if… - AI agents are actively solving real problems across multiple departments using tools you built. - Internal teams are saving meaningful time through automated workflows you've designed and shipped. - Your tool APIs are robust, well-documented, and trusted by both engineers and the AI orchestration layer. - You're recognized as a go-to person for backend architecture and AI tool integration within the team. Desired experience and competencies What you'll need to bring Core engineering skills

Strong backend experience: C#, .NET Core / .NET 6+, ASP.NET Web API, microservices - Solid experience with relational databases (MSSQL and/or PostgreSQL), query design, schema design, performance - Proven ability to design and build REST APIs and integrate with third-party services and webhooks - Familiarity with async programming, event-driven architecture, and message queues (e.g., Azure Service Bus, RabbitMQ) - Some frontend exposure (Vue.js and/or TypeScript), enough to contribute when needed, but the focus here is backend AI tool & integration skills - Practical understanding of

LanguageWire is a global leader in language and content services, dedicated to helping brands achieve their full global potential. Since 2000, we've evolved and built unique technology that enables seamless multilingual communication. Our worldwide network of language experts, together with our intuitive online platform, empowers brands to translate and manage multilingual content effortlessly. The role you'll play As a Full Stack Engineer in the Core Platform Team, you'll be at the forefront of how LanguageWire puts AI to work across the business. You won't be training models or doing data science, you'll be building the tools, services, and workflows that let AI agents actually do things: query internal systems, interact with third-party APIs, automate cross-department workflows, and solve real business problems end-to-end. You'll own the design and delivery of backend-heavy services that expose well-defined, reliable tool interfaces consumed by AI agents. You'll collaborate with product managers, domain experts from across the company, and fellow engineers to identify pain points and turn them into agentic solutions. The team you'll be a part of You'll join the Core Platform Team, one of the foundational engineering teams at LanguageWire. The team owns and operates 12 microservices that underpin the entire organisation, covering file storage, authentication, notifications, email delivery, and integrations with our ERP and CRM systems, among others. These are the services every other team depends on. As LanguageWire invests in AI, the Core Platform Team is taking on a new function: building the tools and integrations that allow AI agents to interact with our services and automate workflows across departments. You'll be at the forefront of that expansion, turning the platform's capabilities into reliable, well-defined interfaces that AI agents can act on. We value outcomes over output, shared ownership over silos, and continuous delivery over long release cycles. Our stack is built on .NET microservices, Azure, and modern API patterns. If you want to make a difference, make it with us by… - Design and build AI agent tools : Create backend services and APIs that AI agents can invoke to query data, trigger workflows, and interact with internal and external systems. - Integrate with existing services and APIs : Connect agent tooling to LanguageWire's internal platform APIs, third-party services, and databases. - Define tool schemas and contracts : Design clear, well-documented tool interfaces (function schemas, OpenAPI specs, MCP server definitions) that LLMs and orchestration layers can reliably consume. - Build agentic features and workflows : Implement end-to-end automated workflows where AI agents solve specific business cases across departments, from operations and finance to sales and project management. - Own your services end-to-end : From architecture and coding to deployment, monitoring, and maintenance ("You build it, you run