OOLIO
Website:
oolio.com
Job details:
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.
JOB DESCRIPTION:
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.
RESPONSIBILITIES:
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.
REQUIREMENTS:
Role: Lead GoLang Developer – AI-Native Engineering – final role will depend on candidate experience, credentials and interview outcomes
Experience: 5 – 10 Years (strong hands-on backend engineering with deep AI focus)
Education: Preferred – B.Tech/B.E/M.Tech/M.E/MCA/M.S
Technology Stack:
(A) Core Engineering:
Golang, Microservices, Distributed Systems, Event-Driven Architecture, REST APIs, gRPC, System Design (HLD/LLD), Performance Optimization, Testing Frameworks
(B) AI & Engineering Productivity:
OpenAI, Anthropic, Gemini, Open-Source LLMs (Llama, Mistral, etc.), LangChain, LangGraph, OpenHands, AutoGen, AI Coding Assistants, Agentic Workflows, RAG Concepts, Vector Databases, AI-Powered Development Tooling
(C) Good to Have:
ReactJS, NodeJS, GraphQL
OTHER REQUIREMENTS:
- Strong hands-on experience building production-grade backend systems in SaaS or product-based organisations.
- Deep expertise in Golang, including concurrency patterns, goroutines, channels, memory management, profiling, and performance optimization.
- Proven experience designing and operating scalable microservices and distributed systems.
- Strong understanding of API design, asynchronous processing, event-driven architectures, and system reliability principles.
- Experience with PostgreSQL, DynamoDB, or similar databases, including schema design, indexing, and query optimization.
- Hands-on experience with AWS, Kubernetes, Docker, and cloud-native application development.
- Strong experience implementing and improving CI/CD pipelines and modern software delivery practices.
- Strong understanding of observability, monitoring, logging, tracing, fault tolerance, and production reliability.
- Experience troubleshooting complex production issues, conducting root cause analysis, and implementing long-term improvements.
- Experience working in high-scale or high-transaction environments is highly desirable.
- Practical experience leveraging AI technologies to improve software engineering productivity and software delivery outcomes.
- Experience building or integrating AI-powered workflows across development, testing, documentation, deployment, observability, or operational processes.
- Familiarity with modern LLM ecosystems, AI coding assistants, agent-based systems, and engineering automation platforms.
- Understanding of concepts such as RAG, context engineering, prompt orchestration, evaluation frameworks, and autonomous workflows.
- Demonstrated ability to identify SDLC bottlenecks and solve them through intelligent automation and AI-driven solutions.
- Passion for exploring emerging AI capabilities and applying them pragmatically to real-world engineering challenges.
- Ability to balance traditional software engineering principles with AI-native development practices.
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