AI Productivity Engineer - San Francisco or Bellevue

Aircall is a unicorn AI-powered customer communications platform used by 22,000+ companies worldwide to drive revenue, faster resolutions, and scale. We’re redefining what a customer communications platform can be—by combining voice, SMS, WhatsApp, and AI into one seamless workspace.
Our momentum comes from a simple but powerful idea: help every customer-facing team work smarter, not harder. Aircall’s AI Voice Agent automates routine calls, AI Assist streamlines post-call tasks, and AI Assist Pro delivers real-time guidance that helps people do their best work. The result—companies grow revenue, deliver faster resolutions, and scale service.
We’ve built a product customers love and a business that scales fast. Aircall operates in nine global offices (Paris, New York, San Francisco, Sydney, Madrid, London, Berlin, Seattle, and Mexico City), and is backed by world-class investors. Our teams are shipping AI innovation faster than ever and expanding across new product lines and markets.
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.
How We Work at Aircall: At Aircall, we believe in customer obsession, continuous learning, and delivering extraordinary outcomes. 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
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 is not a research role and not a customer-facing product AI role.You will build practical, production-grade AI solutions that engineers use daily, and you will be accountable for their real-world adoption and impact.
This role is about using AI to make engineers more effective, not about chasing trends.If you enjoy building real systems that people rely on every day — this role is for you.

What You'll Do

• 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

What We’re Looking For - Required Experience

• 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

Nice to Have

• 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
• Exposur