Key skills
GenAI systemsProduction-grade systemsLLMs OpenAILLMs AnthropicBackend engineeringDeveloper tools experienceAI-powered toolingAPIsservicesproduct mindsetCollaboration skillsProblem-solving skillsAIGenAIOpenAIAnthropicGitHubGitLabJiraCI/CD
About this role
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