- Pro-code leadership: Stay deeply hands-on: write production code, review critical changes, debug complex failures, and set the example for pro-code craftsmanship. - Mentoring: Mentor engineers across ITP on effective pro-code practice, covering design, code quality, testing, debugging, secure coding, performance, and AI-assisted development. Do this through code and design reviews, pairing, tech talks, and communities of practice. - Standards: Develop, publish, and update standards for effective pro-code development. These cover coding conventions, review practices, test automation, CI/CD, observability, secure and Responsible AI development, and AI-assisted coding. Drive adoption and measure it (code health, defect escape rate, lead time). - Legacy understanding: Build a deep understanding of mature and legacy codebases, including historical design decisions, compatibility requirements, undocumented dependencies, and operational constraints. - Modernization approaches: Define and share repeatable modernization approaches: architectural seams and strangler patterns, characterization tests, incremental refactoring, redesigning untestable code, untangling dependencies, and carefully managed migrations rather than unnecessary rewrites. Turn the lessons learned into playbooks and reference implementations. - Tooling: Find friction in the developer workflow and drive the creation of tools, automation, templates, analyzers, and AI-assisted agents and skills. These should speed up code comprehension, test generation, migration, refactoring, review, and release. - Prototyping: Prototype solutions to the hardest software engineering problems, such as novel architectures, performance and scale limits, AI integration patterns, and modernization strategies. Prove feasibility, surface risks, and hand off clear designs and reference code to delivery teams or product groups. - Design and build scalable, resilient, secure, and cost-effective services, APIs, orchestration layers, agent runtimes, retrieval systems, and evaluation frameworks. Balance new capabilities against performance, reliability, maintainability, cost, and backward compatibility. - Diagnose and resolve complex cross-system issues involving concurrency, distributed state, dependencies, performance, data quality, and model behavior. - Establish safe deployment, progressive exposure, rollback, recovery, monitoring, and end-to-end testing practices for critical production changes. - Use telemetry, incident learnings, and quality indicators to measure and improve engineering effectiveness, including the velocity and quality impact of AI-assisted development. To be successful in this role: In this pro-code engineering role, candidates have recent, substantial hands-on experience developing production software, demonstrated success working within and modernizing large, mature, or legacy codebases, and a track record of multiplying the pro-code effectiveness of others through mentoring, standards, and tooling. Experience limited primarily to low-code platforms, solution configuration, prompt engineering, or proof-of-concept development will not meet the core requirements of this role. Bachelor's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. Master's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR Bachelor's Degree in Computer Science or related technical field AND 12+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. Extensive experience designing, building, shipping, and operating large-scale server, cloud, or distributed software systems. Deep, extensive pro-code experience, including substantial recent hands-on work writing, reviewing, debugging, testing, and operating production software in one or more languages such as C++, C#, Java, Go, Rust, or Python. Proven experience working in large, mature, or legacy codebases (tracing unfamiliar execution paths, understanding historical architecture, managing complex dependencies), with concrete, successful approaches to modernizing them while keeping services available and compatible. Experience defining engineering standards and best practices and driving their adoption across multiple teams. Experience building or significantly improving developer tools, build and test infrastructure, or engineering automation. Experience quickly prototyping solutions to ambiguous, technically hard problems and turning them into production-quality designs. Experience with production engineering practices, including test automation, continuous integration, safe deployment and rollback, logging and telemetry, incident response, performance analysis, experimentation, and secure development. Demonstrated ability to apply security, privacy, safety, accessibility, and Responsible AI requirements throughout the software-development lifecycle. Experience communicating complex technical concepts and influencing engineering and business leaders across organizational boundaries. Deep expertise in server architecture, distributed systems, concurrency, storage, networking, API design, or high-scale cloud services. Experience building production AI systems using large language models, retrieval-augmented generation, agent orchestration, tool integration, model gateways, prompt management, or AI evaluation. Experience integrating AI capabilities into established enterprise products rather than developing only greenfield demonstrations or prototypes. Experience creating architectural seams and migration strategies that enable gradual modernization of tightly coupled systems. Experience applying AI-assisted engineering tools to understand, test, document, and safely improve large codebases. Knowledge of Responsible AI and AI safety practices, privacy, security, accessibility, compliance, and data-governance requirements for enterprise AI systems. Demonstrated ability to improve engineering effectiveness, code health, reliability, and developer experience across a broad organization. Experience building internal developer platforms, golden paths, code analyzers, or AI-assisted developer tools. Experience leading engineering guilds, communities of practice, or technical training programs. Strength in systems thinking, architecture design, resilient design, algorithm design, automation, data-driven problem solving, strategic technical implementation, communication, and negotiation.