Full-Stack Lead Engineer for Code Reviewer Project

We are seeking a senior software engineer to lead the development of a robust, multi-language code reviewer. The ideal candidate should excel in agile workflows, code review best practices, and software engineering life cycles. You will work with stakeholders to refine requirements and deliver an adaptable review tool that integrates seamlessly with various CI/CD pipelines (GitLab, GitHub, Gerrit, Jenkins) and supports deep code analysis—potentially leveraging AI/LLM techniques where beneficial.

Key Responsibilities

1. Code Reviewer Development

• Design and implement a system capable of reviewing code across multiple languages and projects.
• Incorporate advanced code analysis techniques (e.g., AST parsing, control flow analysis) to identify bugs, style inconsistencies, and potential security and performance issues.
• Ensure the reviewer can be extended or customized based on evolving requirements from diverse teams.

1. Agile & DevOps Integration

• Employ agile methodologies (Scrum/Kanban) to plan, track, and iterate on new features.
• Integrate seamlessly with CI/CD pipelines (GitHub, GitLab, Gerrit, Jenkins), making the reviewer easily adoptable by various R&D teams.
• Collaborate with DevOps to ensure smooth deployment and maintenance.

1. Expert Code Review Practices

• Define and maintain code review standards, including style guidelines, security checks, and performance considerations.
• Work with development teams to refine pull/merge request processes, fostering a culture of high-quality code.
• Provide comprehensive review feedback and assist in automating repetitive checks to streamline manual review efforts.

1. Cross-Team Collaboration

• Engage with stakeholders and engineering teams to gather feedback and requirements.
• Communicate progress clearly and adapt the solution based on iterative input.
• Advocate for best practices, teaching others how to maximize the value of automated reviews.

1. Research & Prototyping

• Investigate emerging tools and methodologies in static analysis, linting, and machine learning for potential integration.
• Prototype new features or approaches including AI/LLM usage to for code reviewing and unstructured data handling.