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Salary: EUR 5,500 - 7,000 / monthly
IPRally is building AI-powered tools for understanding patents and technical knowledge. The company is growing its Product Engineering team and seeking one to three systems-focused engineers.
In this role, you will maintain the big picture of how IPRally's product, data, and AI agents work together, making technical decisions that enable product teams to move quickly without compromising system reliability. You'll design architecture for both people and agents—establishing boundaries, abstractions, and shared guardrails that allow agents to build safely across the entire system. You'll review code and agent-generated work with the whole system in mind, considering not just functionality but failure modes, security, performance, and cost.
You'll make important technical decisions explicit so that both engineers and AI agents can build on them. The role involves taking ownership of problems that span multiple features or components, and helping shape how the team approaches agentic software development.
IPRally's current stack includes Clojure, ClojureScript, Python, Postgres, Temporal, and GCP. The company emphasizes that engineers are not measured by lines of code written, but by their ability to understand systems, make sound technical decisions, and use available tools—including AI agents—to deliver reliable software. AI agents are already a normal part of how the team builds, and you'll have considerable room to experiment with how you work.
The role reports to the Engineering Manager, Product Engineering.
REQUIREMENTS:
- Strong software engineering background with demonstrated experience designing, building, and operating production systems
- Natural systems-thinking approach, including understanding of data flow, dependencies, failure modes, security, performance, and cost
- Ability to make architectural decisions that balance current needs with future evolution, without overengineering
- Strong communication skills to explain complex systems and technical decisions clearly to other engineers
- Active, meaningful use of AI coding agents as part of day-to-day engineering work, with understanding of both capabilities and failure modes
- Interest in designing systems and engineering practices that allow agents to take on more implementation work safely
- Comfort taking ownership of cross-component problems
- Experience with search, ML systems, or large datasets is advantageous but not required
- No prior experience with the current tech stack or patent technology is necessary; engineering judgment and learning ability are prioritized
