Lead Backend Engineer (Golang) – AI-Native Engineering

ABOUT OOLIO: Oolio is a leading B2B SaaS platform transforming how hospitality venues operate and grow. Trusted by more than 22,000 venues, we power mission-critical POS, payments, online ordering, kiosks, loyalty, kitchen management, and real-time insights — all within one connected ecosystem. We are building the operating system for modern hospitality — simplifying complex operations, accelerating service, and unlocking smarter, data-driven decisions. Built by hospitality professionals with decades of industry experience, we understand the realities of every shift, every service rush, and every guest interaction. From cafés and quick-service restaurants to pubs, multi-site groups, and stadiums, Oolio enables venues to operate seamlessly at scale. With next-business-day settlements, powerful third-party integrations, and 24/7 real human support, we go beyond software — we become long-term partners in growth. As a rapidly scaling product-led organisation, we’re shaping the future of hospitality technology. We build the technology backbone that powers modern hospitality businesses to perform, compete, and thrive at scale.

: At Oolio, Lead Backend Engineer (Golang) – AI-Native Engineering are product builders who combine deep software engineering expertise with modern AI-powered development practices. You will design, build, and operate highly scalable backend systems while leveraging AI, automation, and agentic workflows to accelerate software delivery, improve engineering productivity, and enhance operational excellence. You will move beyond traditional software development by identifying opportunities across the software development lifecycle (SDLC) where AI can reduce manual effort, shorten delivery cycles, improve quality, and increase reliability. As a core member of the engineering team, you will collaborate closely with Product, Design, Platform, and Engineering teams to build both customer-facing systems and internal AI-powered engineering capabilities.

: Backend Engineering • Own end-to-end development of backend services—from architecture and design to deployment, observability, and production reliability. • Design and implement scalable, distributed microservices using Golang with strong emphasis on performance, concurrency, and maintainability. • Architect and evolve event-driven systems using async messaging, queues, pub/sub, and streaming patterns to support high-scale, low-latency applications. • Build and optimize RESTful and/or gRPC APIs that power web, mobile, and third-party integrations. • Contribute across the stack when required, collaborating with React and Node.js services to deliver cohesive product features. • Develop cloud-native services deployed on AWS, leveraging Kubernetes and Docker for containerized infrastructure. • Optimize application performance, throughput, scalability, and reliability as product usage and customer scale increase. • Implement strong testing strategies including unit, integration, contract, and performance testing. • Drive observability best practices including structured logging, monitoring, metrics, tracing, and alerting. • Participate in code reviews and actively contribute to improving engineering standards and best practices. • Troubleshoot complex production issues, perform root cause analysis, and implement long-term systemic fixes. AI-Native Engineering & SDLC Acceleration • Design and implement AI-powered engineering workflows that improve software delivery velocity and engineering productivity. • Build internal tools, agents, and automation systems that assist with software development, testing, documentation, deployment, and operational workflows. • Develop AI-assisted solutions for code generation, code reviews, test creation, debugging, and technical documentation. • Design and implement intelligent CI/CD workflows that leverage AI to improve deployment confidence and reduce release cycle times. • Build AI-powered incident analysis and operational automation capabilities using logs, metrics, traces, and production telemetry. • Develop agentic workflows capable of interacting with code repositories, ticketing systems, deployment pipelines, and engineering platforms. • Evaluate and integrate modern AI technologies, frameworks, and tooling into the engineering ecosystem. • Identify bottlenecks across the SDLC and implement AI-driven solutions that improve efficiency, quality, and developer experience. • Drive adoption of AI-native software engineering practices and continuously improve engineering effectiveness across teams.