aircall - AI Productivity Engineer
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Requirements
• 5+ years of experience as a software engineer, with recent focus on GenAI systems • Strong experience building production-grade systems, not just prototypes • LLMs (OpenAI, Anthropic, etc.) • Prompting, retrieval, and context injection • AI-powered tooling or internal platforms • Solid backend engineering skills (APIs, services, integrations) • Experience working with developer tools (CI/CD, GitHub/GitLab, Jira, observability) • Strong product mindset and comfort operating in ambiguous problem spaces • Particularly interesting profiles are engineers who have built developer tools and are now evolving toward AI-native system design. • Prior experience building developer tools, internal platforms, or DevEx tooling • Experience evolving traditional tooling into AI-assisted or AI-driven workflows • Familiarity with MCP, agent-based systems, or model orchestration concepts • Experience integrating AI with large codebases, monorepos, or complex CI/CD environments • Exposure to security, privacy, and trust considerations in internal AI systems • ## How You’ll Be Successful • AI solutions you build are widely adopted and used regularly by engineers • Engineering productivity measurably improves, using: • existing metrics we already track (e.g. DevEx, CI, delivery, quality, flow), and/or • new, clearly defined metrics you help introduce to capture AI impact • Manual, repetitive workflows are reduced or eliminated, with clear before/after comparisons • Engineering time is visibly saved and reinvested into higher-value work • Improvements are demonstrated with data, not just qualitative feedback • Adoption grows organically because tools are useful, fast, and well-integrated into existing workflows • ## Team & Environment • You’ll join the Engineering Productivity team • You’ll work closely with engineers across the company • Strong collaboration with Infrastructure and Security teams • Product-oriented culture focused on outcomes, not hype
Responsibilities
• Take clear ownership of rapid AI adoption across the engineering organization • Identify high-friction areas in engineering workflows where AI can meaningfully improve productivity • Design and build practical, production-grade AI-powered developer tooling (coding, testing, PR reviews, debugging) • Build contextual, system-aware AI assistants using internal data, codebases, and tooling • Explore, prototype, and productionize AI-driven solutions with strong autonomy on how problems are solved • Automate and streamline workflows across GitLab, Jira, CI/CD, Slack, and observability tools • Design and operate internal AI services and orchestration layers (e.g. MCP servers) • Own solutions end-to-end: discovery → design → build → measure → iterate • Work hands-on with engineering teams to remove friction, enable usage, and move tools from delivery to daily practice • Measure success through adoption, impact, and tangible time saved for engineers • ## What You Won't Do • Build AI features for customer-facing products • Work on speculative AI research without clear outcomes • Act as a general internal support team • Own generic ML infrastructure unrelated to developer productivity
Benefits
• 🚀 Key moment to join Aircall in terms of growth and opportunities • 💆♀️ Our people matter, work-life balance is important at Aircall • 📚 Fast-learning environment, entrepreneurial and strong team spirit • 🌍 45+ Nationalities: cosmopolite & multi-cultural mindset • DE&I Statement:
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