AI Productivity Engineer

Aircall is a unicorn, AI-powered customer communications platform used by 22,000+ companies worldwide to drive revenue, resolve issues faster, and scale customer-facing teams. We’re redefining customer communications by bringing voice, SMS, WhatsApp, and AI together into one seamless workspace. Our momentum comes from a simple idea: help teams work smarter, not harder. Aircall’s AI Voice Agent automates routine calls, AI Assist streamlines post-call work, and AI Assist Pro delivers real-time guidance so people can do their best work. The result is higher revenue, faster resolutions, and teams that scale with confidence. Aircall is headquartered in Paris, our European HQ, with a strong North American presence anchored in Seattle, our North American HQ, and teams across Madrid, London, Berlin, San Francisco, New York City, Sydney, and Mexico City. We’ve built a product customers love and a business that’s scaling quickly, backed by world-class investors and driven by rapid AI innovation across multiple product lines. At Aircall, you’ll join a company in motion. We’re ambitious, product-driven, and execution-focused, with visible impact, fast decisions, and real growth. How we work at Aircall: We’re customer-obsessed, data-driven, and focused on delivering meaningful outcomes. We value ownership, continuous learning, and thoughtful speed. If you thrive in a collaborative, fast-moving environment where trust and impact matter, you’ll feel at home here. 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. Why join us? 🚀 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 💵 Competitive salary package & benefits 🏨 Medical, dental, and vision insurance is 100% covered 📈 401k plan with company matching! ✈️ Unlimited PTO — take the time you need to come to work feeling great! ⭐️ Wellness, commuter, and childcare reimbursements 💚 Generous parental leave policy DE&I Statement: At Aircall, we believe diversity, equity and inclusion – irrespective of origins, identity, background and orientations – are core to our journey. We pride ourselves on promoting active inclusion within our business to foster a strong sense of belonging for all. We’re working to create a place filled with diverse people who can enrich and learn from one another. We’re committed to ensuring that everyone not only has a seat at the table but is valued and respected at it by providing equal opportunities to develop and thrive. We will constantly challenge ourselves to make sure that we live up to our ambitions around diversity, equity and inclusion, and keep this conversation open. Above all else, we understand and acknowledge that we have work to do and much to learn. Want to know more about candidate privacy? Find our Candidate Privacy Notice here. 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 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 Location United States (preferred: Seattle or San Francisco) Open to strong US-based candidates in other locations Collaboration with teams in Europe expected

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      "text": "What You'll Do",
      "content": "\n<li>Take <strong>clear ownership of rapid AI adoption across the engineering organization</strong></li>\n<li>Identify high-friction areas in engineering workflows where AI can meaningfully improve productivity</li>\n<li>Design and build <strong>practical, production-grade AI-powered developer tooling</strong> (coding, testing, PR reviews, debugging)</li>\n<li>Build contextual, system-aware AI assistants using internal data, codebases, and tooling</li>\n<li>Explore, prototype, and productionize AI-driven solutions with strong autonomy on <em>how</em> problems are solved</li>\n<li>Automate and streamline workflows across GitLab, Jira, CI/CD, Slack, and observability tools</li>\n<li>Design and operate internal AI services and orchestration layers (e.g. MCP servers)</li>\n<li>Own solutions end-to-end: discovery → design → build → measure → iterate</li>\n<li>Work hands-on with engineering teams to remove friction, enable usage, and move tools from delivery to daily practice</li>\n<li>Measure success through <strong>adoption, impact, and tangible time saved for engineers</strong></li>\n"
    },
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      "text": "What You Won't Do",
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    {
      "text": "What We’re Looking For - Required Experience",
      "content": "\n<li>5+ years of experience as a software engineer, with recent focus on GenAI systems</li>\n<li>Strong experience building <strong>production-grade systems</strong>, not just prototypes</li>\n<li>Hands-on experience with:</li>\n<li>LLMs (OpenAI, Anthropic, etc.)</li>\n<li>Prompting, retrieval, and context injection</li>\n<li>AI-powered tooling or internal platforms</li>\n<li>Solid backend engineering skills (APIs, services, integrations)</li>\n<li>Experience working with developer tools (CI/CD, GitHub/GitLab, Jira, observability)</li>\n<li>Strong product mindset and comfort operating in ambiguous problem spaces</li>\n"
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      "text": "Nice to Have",
      "content": "\n<li>Particularly interesting profiles are engineers who have <strong>built developer tools</strong> and are now evolving toward <strong>AI-native system design</strong>.</li>\n<li>Prior experience building developer tools, internal platforms, or DevEx tooling</li>\n<li>Experience evolving traditional tooling into <strong>AI-assisted or AI-driven workflows</strong></li>\n<li>Familiarity with MCP, agent-based systems, or model orchestration concepts</li>\n<li>Experience integrating AI with large codebases, monorepos, or complex CI/CD environments</li>\n<li>Exposure to security, privacy, and trust considerations in internal AI systems</li>\n"
    },
    {
      "text": "How You’ll Be Successful",
      "content": "\n<li>AI solutions you build are <strong>widely adopted and used regularly by engineers</strong></li>\n<li><strong>Engineering productivity measurably improves</strong>, using:</li>\n<li>existing metrics we already track (e.g. DevEx, CI, delivery, quality, flow), and/or</li>\n<li>new, clearly defined metrics you help introduce to capture AI impact</li>\n<li>Manual, repetitive workflows are reduced or eliminated, with clear before/after comparisons</li>\n<li>Engineering time is visibly saved and reinvested into higher-value work</li>\n<li>Improvements are demonstrated with <strong>data</strong>, not just qualitative feedback</li>\n<li>Adoption grows organically because tools are useful, fast, and well-integrated into existing workflows</li>\n"
    },
    {
      "text": "Team & Environment",
      "content": "\n<li>You’ll join the Engineering Productivity team</li>\n<li>You’ll work closely with engineers across the company</li>\n<li>Strong collaboration with Infrastructure and Security teams</li>\n<li>Product-oriented culture focused on outcomes, not hype</li>\n"
    },
    {
      "text": "Location",
      "content": "\n<li>United States (preferred: Seattle or San Francisco)</li>\n<li>Open to strong US-based candidates in other locations</li>\n<li>Collaboration with teams in Europe expected</li>\n"
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