About the role
We're building complex, production-grade AI features into our platform, and we're looking for a strong full-stack agentic engineer to design, ship, and own them end-to-end — from the agent and backend logic all the way to the UI client.
This is a close-the-loop role: you should be just as comfortable building the service that orchestrates an LLM call as you are building the UI components that present its results to the user. It's first and foremost a software engineering role — we want real engineering talent that has made the shift to modern, agentic development: directing agentic coding tools, reviewing their output, and holding the result to production standards.
What You’ll Do
Build features into a complex, production-grade agentic system — not a simple chatbot. Agents here use tools, share context, and execute multi-step workflows, and your work plugs into that architecture.
Own features from agent to UI: design backend services and AI integrations, build the client experience, and connect the two into something that works reliably in production.
Build primarily through agentic coding tools — guiding, reviewing, and testing their output with the same discipline as hand-written code.
Integrate AI capabilities — LLM calls, structured outputs, tool/function calling, basic retrieval — sensibly, and know when a deterministic solution is the better choice.
Requirements
4+ years of professional full-stack software engineering experience with strong programming fundamentals.
Proven experience delivering features end-to-end: backend services and APIs and modern frontend development (e.g. Angular, React, Vue, or similar).
Practical, daily use of agentic coding tools such as Claude Code, Cursor, Copilot Agent Mode, Codex, or similar.
Clear communication with both technical and non-technical stakeholders.
Nice to Have
Experience with multi-agent workflows, tool-using agents, or RAG pipelines.
Familiarity with orchestration frameworks (LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI, or MCP-based tools).
Hands-on experience building AI-powered products or features (e.g. integrating an LLM, prompts, structured outputs).
UI for AI features — streaming responses, chat interfaces, human-in-the-loop flows.
Angular experience.
.NET Core backend development experience.
CI/CD, feature flags, TDD, and production monitoring.
