Ai-First Fullstack Engineer (Senior)

About Livo Livo is building the AI-powered operating system for the healthcare workforce.We operate at the intersection of HR Tech, marketplaces, and applied AI, tackling one of healthcare’s hardest problems: enabling hospitals to plan, staff, and operate their workforce effectively.By putting clinicians first, we help nurses and doctors focus on what truly matters-patient care.We are a fast-scaling company where advanced AI meets real-world impact.About the RoleWe’re looking for a hands-on senior full-stack engineer, highly productive across frontend, backend, data, and cloud, who treats AI as a core part of the software development lifecycle.This isn’t about experimenting with AI on the side.We expect engineers to actively leverage AI-powered tools across design, implementation, testing, debugging, documentation, and operations, while maintaining strong engineering judgment and accountability for production outcomes.What you’ll be doingOwn outcomes at the product and company level, not just your own tasks or services.High ownership is the bar here.Design, build, test, deploy, and operate full-stack features end to end.Build scalable frontend experiences and the backend services, APIs, and integrations behind them.Model and optimize relational data for complex, real-time workflows.Participate in architecture discussions and own the technical decisions for what you ship.Meet the performance, reliability, security, and compliance bar required for sensitive healthcare data.Work closely with account managers and fellow engineers to turn needs into shipped solutions.AI-Fluent EngineeringUse AI-assisted development tools as a standard part of your daily workflow.Leverage AI for code generation and refactoring, test creation, documentation, debugging and root-cause analysis, system-design exploration, and data analysis.Critically evaluate AI-generated output and validate correctness before it ships.Continuously explore new AI tools and capabilities, and identify where they can improve engineering efficiency and product quality.Contribute to our best practices for AI-assisted software development.What you should bring along5+ years of professional software engineering experience, including production ownership of web applications.Strong proficiency in JavaScript / TypeScript and a modern frontend framework (React preferred).Solid backend experience (Java/Kotlin, Python, or Go) with REST API and distributed-systems design.Strong SQL and schema design (MySQL/Postgres), comfortable modeling complex workflows.Comfort deploying and operating applications in the cloud (AWS or GCP).Strong problem-solving and debugging skills, and a bias toward clean, well-tested code that balances time-to-market with quality.Regular use of AI coding assistants in professional work: effective prompting and iteration, a clear sense of where LLMs help and where they fall short, and the ability to verify, test, and safely operationalize their output.A product mindset and the communication skills to work closely with product, design, and operations.Nice to haveContainerization and orchestration.Building AI-powered features or integrating LLM APIs into production.RAG, vector databases, agent workflows, or AI evaluation frameworks.Constraint-solving, scheduling, or logistics systems.Fast-paced startup environments.#J-*****-Ljbffr

5+ years of professional software engineering experience, including production ownership of web applications. Strong proficiency in JavaScript / TypeScript and a modern frontend framework (React preferred). Solid backend experience (Java/Kotlin, Python, or Go) with REST API and distributed-systems design. Strong SQL and schema design (MySQL/Postgres), comfortable modeling complex workflows. Comfort deploying and operating applications in the cloud (AWS or GCP). Strong problem-solving and debugging skills, and a bias toward clean, well-tested code that balances time-to-market with quality. Regular use of AI coding assistants in professional work: effective prompting and iteration, a clear sense of where LLMs help and where they fall short, and the ability to verify, test, and safely operationalize their output. A product mindset and the communication skills to work closely with product, design, and operations. Nice to have Containerization and orchestration. Building AI-powered features or integrating LLM APIs into production. RAG, vector databases, agent workflows, or AI evaluation frameworks. Constraint-solving, scheduling, or logistics systems. Fast-paced startup environments. #J-*****-Ljbffr