Senior Software Engineer - Kafka

Gravitee•Denver, CO

8h•$160,000•Hybrid

About The Position

Gravitee is a 2025 Gartner® Magic Quadrant™ Leader, on a mission to govern the world’s intelligence. We deliver the industry’s most advanced platform for Any API, Any Event, and Any AI Agent, trusted by global leaders like Michelin, Roche, and Blue Yonder. The Mission: We are the first to bridge traditional API Management with the new frontier of AI Agent Security. The Momentum: A high-growth Leader - combining market credibility with startup speed. The DNA: We hire people who Hold Nothing Back - passionate builders who want to redefine digital infrastructure. Don’t just watch the AI revolution. Build the infrastructure that controls and secures it. We are looking for a Senior Software Engineer to build product experiences and the APIs behind them. You will turn operational data into clear visualizations and help customers configure and govern APIs and AI agents. As Gravitee expands its AI Agent Management capabilities, you will help build interfaces for configuring AI Gateways, governing MCP and agent-to-agent interactions, discovering tools and agents, and monitoring how agentic systems behave in production. This is a hands-on, full-stack role for an engineer who is comfortable moving between frontend experiences, backend APIs, data models, and technical design.

Requirements

Significant experience building and operating full-stack applications in a production environment; typically five or more years for this level.
Strong Java experience and experience designing maintainable REST APIs and backend services.
Strong TypeScript and modern frontend development experience using a component-based framework.
Experience building data-rich user interfaces and visualizations, including transforming complex data into clear customer-facing insights.
A solid understanding of API design, data modeling, distributed systems, and asynchronous workflows.
Experience with automated testing, Git-based development, code review, and iterative product delivery.
Experience working with persistent data stores such as MongoDB or relational databases, and with search or analytics technologies such as Elasticsearch or OpenSearch.
Hands-on experience using LLMs or AI coding assistants as part of an engineering workflow, combined with the judgment to review and improve their output.
Clear communication, collaborative problem-solving, and the ability to take an ambiguous problem through to production.

Nice To Haves

Angular or another modern component-based frontend framework.
Asynchronous and non-blocking Java development using Vert.x, RxJava, Netty, or comparable reactive technologies.
Docker, Kubernetes, and cloud-native application delivery.
API management, API gateways, OpenAPI, or event-streaming platforms such as Kafka.
API security and identity standards such as OAuth 2.0 and OpenID Connect.
Model Context Protocol (MCP), Agent2Agent (A2A), tool calling, LLM proxies, or other emerging AI protocols and standards. Prior production experience is not required.
Building or operating LLM-powered applications, RAG systems, or agentic workflows - especially their security, governance, and observability needs.
Open-source software or enterprise developer platforms.

Responsibilities

Design and deliver features end to end, from discovery and technical design through implementation, testing, release, and iteration.
Build clear, responsive interfaces and data visualizations using TypeScript and Angular.
Design and develop Java-based APIs and services that support those product experiences.
Contribute where needed to Gravitee's core gateway runtime, applying asynchronous and non-blocking design patterns to performance-sensitive code.
Help customers configure and observe Gravitee’s AI-related capabilities, including AI Gateways, MCP tools and servers, agent identity, and agent-to-agent interactions.
Model and transform operational data so that complex platform behavior becomes understandable and actionable.
Contribute to both Gravitee’s open-source platform and Enterprise Edition.
Use LLMs and AI-assisted development tools thoughtfully for prototyping, implementation, testing, debugging, and exploration.
Apply sound engineering judgment to validate AI-generated work and maintain high standards for correctness, security, maintainability, and performance.
Write meaningful automated tests and contribute to reliable delivery practices.
Collaborate with product managers, designers, engineers, and technical leaders to discover effective solutions and improve them through code and design reviews.
Share what you learn and help the team make practical choices as technologies and protocols evolve.