AI Productivity Engineer, Aircall

• We value open collaboration, taking ownership, and making smart, informed decisions with speed and precision. If you thrive in a fast-paced, team-driven environment where curiosity, trust, and impact matter, you'll fit right in
• If you enjoy building real systems that people rely on every day — this role is for you
• 5+ years of experience as a software engineer, with recent focus on GenAI systems
• Strong experience building production-grade systems, not just prototypes
• Hands-on experience with:
• 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

Desirable

• At Aircall, you’ll join a company in motion—ambitious, profitable, and product-driven—where impact is visible, decisions are fast, and growth is real
• We are hiring a Software AI Engineer to join the Engineering Productivity (EngProd) team at Aircall
• Your mission is to accelerate AI adoption across the engineering organization by building AI-powered tools and systems that measurably improve how engineers work — reducing friction, automating repetitive tasks, and embedding intelligence directly into everyday workflows
• This role is about using AI to make engineers more effective, not about chasing trends
• 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
• 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
• Collaboration with teams in Europe expected