Position: Senior Full Stack AI Software Engineer
About the Role
We are looking for a highly experienced Senior Full Stack Software Engineer to help design, build, extend, and stabilize a highly dynamic, metadata-driven application platform. Our platform is a low-code / no-code / pro-code system (Golang / Python), with a web client (TypeScript / React) that consumes a rich API surface. The platform dynamically renders application pages from YAML-based metadata definitions. A view definition, for example, can declare components, layout, styling, and behavior, and the runtime interprets that metadata to build the user experience on the fly.
In addition to core platform capabilities, our product includes AI-powered application development experiences. Users can leverage AI agents, MCP tools, and related workflows to help build and configure applications. This role will contribute to platform features, integrations, and user experiences that enable those AI-assisted capabilities, while partnering with our AI development team.
Our system is multi-tenant, context-sensitive, and deeply layered. Request execution depends on URL-derived context such as tenant, site, and workspace boundaries. Runtime behavior varies based on metadata, execution context, loaded data, authorization, platform state, and AI-assisted workflows. The complexity is high, and we need someone who can work effectively across the full stack while understanding how changes in one layer affect the entire platform. The primary goal of this role is to deliver features, fix bugs, improve platform stability, and help evolve the architecture of a sophisticated product where application behavior is generated dynamically at runtime. We need someone who is comfortable working in a complex codebase, can reason through layered execution paths, and can make thoughtful changes without introducing regressions.
What You Will Do
- Design, implement, and maintain features across the full stack, including backend services, APIs, and web client behavior
- Work on a metadata-driven runtime that interprets YAML definitions to build dynamic user experiences
- Build and enhance platform capabilities used by low-code / no-code / pro-code application builders
- Implement platform features and user experiences that support AI-assisted application development
- Integrate and extend platform workflows that interact with AI agents, MCP tools, prompts, generated outputs, and related orchestration layers
- Partner closely with the AI development team to expose, consume, and operationalize AI capabilities in the core platform
- Debug and resolve complex defects spanning backend services, request context resolution, metadata processing, data loading, frontend rendering, and AI-related workflows
- Extend and refine the API surface consumed by the web client and other platform layers
- Implement changes that behave correctly across multi-tenant, site, and workspace contexts
- Improve reliability, scalability, and maintainability across the platform
- Diagnose and fix subtle bugs caused by interaction effects between metadata, runtime state, data dependencies, execution context, and AI-assisted flows
- Contribute to architectural improvements that reduce complexity and improve clarity of system behavior
- Partner with product, design, QA, AI engineering, and platform engineering peers to deliver capabilities that are flexible, stable, and testable
- Write high-quality, maintainable code with strong attention to correctness, performance, and security
- Participate in code reviews, technical design discussions, and root-cause analysis of production issues
- Help identify and reduce technical debt in critical areas of the platform
- Support the creation of observability, diagnostics, and guardrails that make the platform easier to operate and evolve
What We Need
We need someone who knows how to build and debug systems where:
- behavior is generated dynamically from metadata
- UI structure is not fully static
- request execution depends on runtime context
- multiple tenant and application boundaries exist
- product behavior includes AI-assisted workflows and integrations
- bugs often emerge from the interaction of layers rather than isolated code paths
You should be comfortable moving between backend and frontend concerns, reading through unfamiliar code paths, and making changes in a platform where understanding context is essential to getting the implementation right.
You do not need to be the person building foundation AI models or core agent infrastructure from scratch, but you should be comfortable integrating AI-powered capabilities into production software and collaborating effectively with specialized AI engineers.
Required Qualifications
- 7+ years of professional software engineering experience
- Strong experience building and maintaining production-grade web applications across backend and frontend layers
- Strong proficiency in Golang, TypeScript & React
- Experience designing and consuming APIs in complex application environments
- Strong experience with modern web application development and frontend architecture
- Experience working in systems with layered architecture, runtime context, and nontrivial request lifecycles
- Strong debugging and problem-solving skills in complex, highly integrated systems
- Experience delivering features and bug fixes in large or sophisticated codebases
- Ability to reason about performance, reliability, correctness, and maintainability
- Experience integrating external or internal platform services into end-user product experiences
- Strong written and verbal communication skills
- Ability to operate independently and contribute effectively in ambiguous technical environments
Preferred Qualifications
- Experience working on low-code / no-code platforms or internal platform products
- Experience with metadata-driven, configuration-driven, or dynamically rendered systems
- Experience with YAML- or JSON-based runtime definitions
- Experience building systems in multi-tenant environments
- Experience with AI-enabled product features, agent-driven workflows, assistant experiences, prompt-driven interfaces, or developer tooling integrations
- Experience integrating with LLM-backed services, AI orchestration layers, or tool invocation frameworks
- Experience with context-sensitive authorization and routing models
- Experience with frontend frameworks used to build dynamic SPAs or complex web clients
- Experience designing extensible backend services and APIs in Golang
- Familiarity with caching, concurrency, and performance optimization in backend systems
- Familiarity with observability tooling, tracing, logs, and metrics
- Experience improving testability and supporting automated testing in complex systems
- Experience working with CI/CD pipelines and modern software delivery practices
