AI Productivity Engineer

About the role

Software AI Engineer in the Engineering Productivity team. Your mission is to accelerate AI adoption across the engineering organization by building practical, production‑grade AI solutions for engineers.

• 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 product ionize AI‑driven solutions with strong autonomy on how problems are solved.
• Automate and streamline workflows across Git Lab, 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.
• 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.

• 5+ years of experience as a software engineer, with recent focus on GenAI systems.
• Strong experience building production‑grade systems.
• 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, Git Hub/Git Lab, 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 Dev Ex 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.
• AI solutions you build are widely adopted and used regularly by engineers.
• Engineering productivity measurably improves using existing metrics (Dev Ex, CI, delivery, quality, flow) and/or new metrics you help introduce.
• 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.

United States (preferred: Seattle or San Francisco). Open to strong US‑based candidates in other locations. Collaboration with teams in Europe is expected.

• Competitive salary package and benefits.
• Medical, dental, and vision insurance 100% covered.
• 401k plan with company matching.
• Unlimited PTO.
• Wellness, commuter, and childcare reimbursements.
• Generous parental leave policy.

At Aircall, we believe diversity, equity and inclusion 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 are committed to ensuring that everyone has a seat at the table and is valued and respected.

Candidate privacy notice is available upon request.