Senior Full - Stack Engineer - LLM - Powered Code Review PoC at Fuku

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.

- 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.

- 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.

- 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.

- 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.