Technical Lead (AI Platform & Developer Experience (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 operationalising 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 DevEx, 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.

What the job involves

• 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 DevEx 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 (DevEx) 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 optimise 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.".