• Drive architectural decisions across the [R]AIMS platform, evaluating tradeoffs across performance, scalability, security, and maintainability and building alignment across engineering and product stakeholders • Lead major technical epics from conception through delivery, decomposing ambiguous problems into executable plans and keeping cross-functional teams moving with clarity and momentum • Simplify and rationalize distributed system architecture as the platform scales, reducing incidental complexity and improving operational reliability without sacrificing capability • Optimize platform performance across both edge and cloud deployment targets, identifying and resolving bottlenecks in data-intensive, latency-sensitive operational environments • Establish strong engineering foundations and reusable technical patterns that improve developer productivity and code quality across the team • Mentor engineers at multiple levels, conducting design reviews, providing substantive code feedback, and actively elevating technical execution across the platform • Partner with AI/ML engineers on model integration, inference optimization, and the operational deployment of agentic workflows within [R]AIMS • Engage directly with customers and program stakeholders at operationally demanding environments across the Department of Defense, representing Raft’s technical capabilities with credibility and clarity

• Own the New Relic query language stack including grammar parsing, compilation, and processing. • Proactively participate in cross-functional committees to move the query language forward, ranging from collaborations with AI, Visualizations, and Data Processing teams. • Build and maintain scalable distributed Java services and Petabyte-scale Infrastructure. • Investigate and resolve critical performance, availability, and resiliency issues and risks in a multi-region, multi-cloud, multi-tenant distributed database. • Working in an agile environment with a DevOps approach — the team builds and maintains their own software, including taking turns with being on-call.

• Design realistic terminal-based benchmark tasks for AI evaluation systems • Create technically deep debugging and investigation scenarios • Develop task specifications involving infrastructure, workflows, pipelines, or operational failures • Write clear solution approaches and deterministic evaluation criteria • Identify realistic edge cases, failure modes, and system constraints • Design multi-step reasoning challenges across complex technical environments • Contribute expertise across one or more engineering or operational domains • Review and refine benchmark quality, difficulty, and validation logic • Collaborate with reviewers and researchers on AI evaluation workflows

• Join our AI Lab as a Tech Lead and help Qonto build the AI-first product that transforms how 600,000+ businesses manage their finances. • Spend most of your time doing what you love — shipping — while providing the technical direction and light mentorship that turns a high-velocity squad into a high-quality one. • Work alongside Sophie, our Manager for the AI Lab, you'll set the technical standard for a 13-person cross-functional team as we scale from one AI agent to many. • Directly manage 5 engineers across Backend, Web, and Mobile — leading by project and track, not by stack — keeping pace with our ambition: one new agent every 6 weeks. • Act as the technical interface between the AI Lab and the broader organisation — understand how features are built across Qonto, bring that knowledge into the Lab, and ensure what we build in agents integrates with the wider product.