AI Productivity Tools Development Engineer

As a member of the AI Infrastructure & Applications team, you will lead the design, development, and production deployment of AI/ML-powered systems alongside the automation infrastructure and developer platforms that support them. You will architect intelligent, scalable solutions used across the organization — driving AI adoption into developer and automation workflows, internal tooling, and domain-specific applications — while also building and maintaining the automation frameworks and infrastructure those systems depend on. The systems you build are expected to be production-grade, reliable, observable, and continuously improving. Minimum Qualifications BSc in Computer Science, Software Engineering, or related field — or equivalent industry experience. 5 years of strong programming, system design, and API design skills with a focus on scalability and production-readiness. Proficiency in Python for automation, API development and pipeline engineering. Experience building automation frameworks, internal developer tools, and shared platforms at scale. Solid understanding of prompt engineering, retrieval strategies, context management, and model orchestration. Hands-on experience building and deploying LLM-powered systems: agentic pipelines, RAG, tool-use, and function-calling. Strong debugging skills across the full AI stack; familiarity with LLM safety and responsible AI practices. Experience designing and running AI evaluations — automated and human-in-the-loop — and embedding quality gates into CI/CD release workflows. Preferred Qualifications Experience leading projects end-to-end — from initial scoping and stakeholder alignment through delivery- coordinating across engineering, product, design, and domain teams. Practical systems management experience: configuration management, dependency resolution, and deployment tooling across cloud and on-prem environments. Ability to design sustainable automation systems serving a large, diverse engineering user base. Hands-on experience with orchestration frameworks and managing the full development lifecycle of complex, multi-component systems. MA in Computer Science, Software Engineering, or related field.