Senior Agentic AI Developer

ITQuarks is an AI-first engineering partner. We design, build, and operate production AI systems for our own products - including EigenVox (author-voice content generation) and DocuGenius (AI claims automation) - and for enterprise clients across Europe. Our work ships to production: multi-tenant, audited, and maintained long after launch.

We are hiring a Senior Agentic AI Developer to own agentic systems end to end, from architecture and solution design through implementation, deployment, and production operation. You will work directly with senior engineers and client stakeholders, and your decisions on architecture, tooling, and integration will shape how these systems run in production.

Responsibilities

• Develop AI systems, agentic AI applications, and LLM-based services
• Design and evolve agent systems, from single-agent workflows to multi-step orchestration
• Build MCP servers and tool integrations
• Implement RAG architectures
• Develop web applications and user interfaces for AI products
• Implement guardrails and security mechanisms for LLM applications
• Containerise and deploy services on Kubernetes and OpenShift
• Build and maintain CI/CD pipelines
• Prepare technical solution designs and documentation
• Execute releases and production deployments
• Advise clients and internal teams on architecture, technology, and integration decisions

Mandatory

• Hands-on agentic engineering - designing and building autonomous, tool-using LLM agents
• Developing MCP (Model Context Protocol) servers
• Building secure web and AI applications
• Containerisation and orchestration using Docker, Kubernetes, or OpenShift
• Developing production-grade LLM-based applications

Preferred

• Experience with AWS Kiro
• Experience with multi-agent systems and agent-to-agent communication
• API and backend integrations for AI applications
• Experience with enterprise AI ecosystems - AI gateways, prompt management, or model governance
• Experience with AWS Bedrock, Azure AI Services, or comparable managed AI platforms
• Experience with observability, monitoring, tracing, and evaluation using Langfuse, Arize, or OpenTelemetry
• Experience with retrieval-augmented generation (RAG)
• Advanced MCP server experience, including operating and integrating complex tool landscapes