AI Productivity Engineer

Aircall is a unicorn, AI-powered customer communications platform used by over 22,000 companies worldwide. They are seeking an AI Productivity Engineer to accelerate AI adoption across the engineering organization by building AI-powered tools and systems that improve engineer productivity and reduce friction in workflows.

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

Requirements:

• 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
• 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