Technical Lead; AI Platform & Developer ; DevEx

Requirements

• Software Engineering Core: 7+ years of foundational software engineering experience. You are an expert backend engineer or systems architect who builds production‑grade software that scales
• AI & LLM Mastery:
  Deep, recent, hands‑on experience ope rationalising Generative AI. You understand context windows, token optimisation, embedding models, vector databases, and RAG architectures
• AI‑Native Workflow Philosophy:
  You are deeply embedded in the modern AI development ecosystem. You have a clear, pragmatic philosophy on how AI‑driven development changes code quality, testing methodologies, and technical debt
• Management experience is a plus (managing engineering teams, with a strong preference for Dev Ex, Platform, Infrastructure, or Tooling teams)
• Care deeply about quality, clarity, and impact
• Are curious, adaptable, and excited by emerging technologies like AI
• Take ownership and follow through
• Value collaboration, openness, and continuous improvement
• Bonus points if you’ve worked with APIs, cloud‑native platforms, AI‑enabled systems, or open source, but curiosity matters most

• At Gravitee, we aren’t just using AI to build features for our users; we are completely reinventing the Software Development Life Cycle (SDLC) itself
• This is not a corporate strategy role or a pure ML research position
• We are looking for a leading, hands‑on AI‑native engineer
• You will lead a high‑performing squad of Dev Ex and Platform Engineers to build an AI‑augmented internal ecosystem that optimises every phase of our lifecycle, from product ideation to production deployment
• Build the Squad:
  Hire, scale, and lead a high‑performing team of Developer Experience (Dev Ex) and Platform Engineers
• Lead by Example:
  Act as a premier AI‑native engineer, establishing the standard for how the team leverages advanced AI tools (e.g., Cursor, Devin, custom LLM workflows) to code faster, safer, and smarter
• Foster User Empathy:
  Ensure your team views internal Product Managers, Designers, and Developers as their primary customers
• Resource Planning:
  Masterfully balance the roadmap between long‑term, high‑leverage R&D (e.g., experimenting with multi‑agent orchestration) and immediate, tactical fixes that unblock the engineering organisation
• Architect the AI‑Native Stack:
  Lead the technical implementation of our Internal Developer Platform (IDP), knowing exactly when to rely on traditional, deterministic backend orchestration vs. autonomous LLM agents
• Master Context Engineering:
  Own our ingestion pipeline. You understand that AI is only as good as its context; you will build robust RAG systems that sync codebase knowledge, Linear/Jira histories, and Architecture Decision Records (ADRs) to feed internal tooling and eliminate hallucinations
• Infrastructure Guardrails:
  Ensure all AI‑generated code and automated workflows are bounded by strict engineering guardrails, robust error handling, and security scanning
• For Product:
  Architect tools that ingest raw customer feedback, telemetry, and business requirements to assist PMs in drafting rigorous, comprehensive Product Requirement Documents (PRDs)
• For Engineering:
  Seamlessly integrate advanced coding assistants that go beyond basic autocomplete, enabling entire scaffolding and test‑suite generation directly mapped to the specs created in the product phase
• Define the Metrics:
  Don’t guess, measure. Establish and track the core "Physics of Gravitee":
  Speed, Velocity, and Quality
• Monitor AI Adoption:
  Track and optimize the usage rates of internal AI tools, acceptance rates of AI‑generated code/specs, and the direct impact on DORA metrics (Deployment Frequency, Lead Time for Changes) and "Time‑to‑Spec."