Job Description

NexaFlow is building a next-generation code review tool powered by large language models. We are seeking a Senior Full-Stack Engineer to lead the development of a proof-of-concept prototype that integrates LLM-based analysis into existing CI/CD workflows.

Responsibilities:
- Design and implement a full-stack application that integrates LLM APIs (OpenAI/Anthropic) for automated code review.
- Build webhook-based integrations with GitHub/GitLab to automatically analyze pull requests.
- Develop a hybrid analysis pipeline combining LLM reasoning with static analysis for bug detection, security vulnerability identification, and code quality enforcement.
- Create a configurable rule engine allowing per-repository custom review policies.
- Build a dashboard displaying review statistics, trends, and actionable insights.
- Containerize the entire solution with Docker for easy self-hosted deployment.
- Own the full lifecycle from architecture design through deployment and documentation.

Requirements:
- 5+ years of professional full-stack development experience.
- Strong experience integrating LLM APIs (OpenAI GPT-4, Anthropic Claude) into production systems.
- Hands-on experience with CI/CD pipeline integration (GitHub Actions, GitLab CI).
- Prior work building code review tools, linters, or static analysis systems.
- Proficiency in Python (FastAPI) or Node.js for backend; React or Vue for frontend.
- Experience with Docker containerization and self-hosted deployment.
- Ability to work independently across the full stack from architecture to deployment.
- Strong communication skills for async collaboration.

Nice to Have:
- Experience with RAG (Retrieval-Augmented Generation) architectures.
- Familiarity with CodeQL, Semgrep, or similar static analysis tools.
- Previous startup or PoC-stage project experience.
- Contributions to open-source developer tools.

Additional Information:
- This is a contract/consulting engagement, not a full-time employee role.
- We value flexibility and outcome-driven collaboration.
- Remote-friendly with a preference for candidates in the San Francisco Bay Area.