• Help train large language models (LLMs) to write production-grade code • Compare and rank multiple code snippets • Repair and refactor AI-generated code for correctness, efficiency, and style • Inject feedback into the RLHF pipeline

• Help train large language models (LLMs) to write production-grade code across a wide range of programming languages: • Compare and rank multiple code snippets, explaining which is best and why • Repair and refactor AI-generated code for correctness, efficiency, and style • Inject feedback (ratings, edits, test results) into the RLHF pipeline and keep it running smoothly • End result: the model learns to propose, critique, and improve code the way you do.

• Maintain and extend GitHub Actions-based CI/CD pipelines • Manage automated workflows for: MultiDev environment creation (Pantheon) • Canary deployments and phased releases • Fleet-wide upstream deployments across 400+ sites • Troubleshoot pipeline failures and improve resiliency and observability • Enhance automation around release processes, including changelog generation and deployment validation • Work with Pantheon platform tooling, including: Terminus CLI • MultiDev environments • Upstream management and site orchestration • Support platform stability, deployment workflows, and environment lifecycle management • Contribute to and maintain a Drupal 10-based multi-tenant platform • Develop and support custom modules, integrations, and backend logic • Debug and resolve issues across a shared codebase impacting multiple sites • Ensure best practices for performance, security, and maintainability • Extend automation using tools like: Quicksilver (event-driven workflows) • Improve visibility into system health, deployment outcomes, and failures

• Architect, build, and maintain robust integrations between our platform and external third-party systems. • Ensure scalability, reliability, and security within our AWS environment. • Design and implement well-structured APIs and services that enable seamless data exchange across platforms, following best practices in versioning, authentication, and error handling. • Build and operate services on AWS with strong CI/CD practices and infrastructure-as-code (IaC) to enable reliable, repeatable deployments. • Implement monitoring, logging, and alerting for integrations. • Investigate incidents, resolve root causes, and proactively harden systems against recurrence. • Use AI tools in day-to-day development (e.g., for code generation, debugging, test creation, and documentation) and help establish practical AI adoption across the team.