AI Software Engineer (MCP Development / AI R&D)

Careerflow.ai

11h•Remote

About The Position

We are looking for a highly skilled Software Engineer to join our client's AI Infrastructure team. In this role, you will be at the forefront of the Model Context Protocol (MCP) movement, building the connective tissue between advanced AI models and enterprise-grade data. You will be responsible for developing, refactoring, and maintaining the tools that allow agentic coding assistants to interact seamlessly with complex database environments.

Requirements

Deep understanding of the Model Context Protocol (MCP) and its application in connecting LLMs to external data sources.
Strong proficiency in SQL and experience working with a variety of database engines (e.g., PostgreSQL, MySQL, BigQuery, Spanner, or NoSQL solutions).
Proven experience in expanding test coverage for complex, asynchronous AI tools and CLI-based assistants.
Familiarity with "Agentic" workflows, including how AI models use tools, manage context, and navigate file systems/databases.
Strong background in refactoring code for scalability and maintaining open-source repositories.

Nice To Haves

Experience with Context Engineering (optimizing token utility and state management for LLM inference).
Active contributor to open-source projects or developer advocacy experience.
Familiarity with Cloud Identity and Access Management (IAM) and observability frameworks (OpenTelemetry).

Responsibilities

Implement a variety of MCP tools for both managed MCP servers and the open-source MCP Toolbox for Databases.
Expand test coverage and functionality for tools and skills utilized by leading agentic coding assistants (e.g., Gemini CLI, Claude Code, and other IDE-integrated agents).
Take ownership of the MCP Toolbox for Databases codebase, focusing on refactoring legacy components and maintaining high-quality test suites.
Bridge the gap between engineering and community adoption by converting technical blog posts into functional toolbox sample code and comprehensive documentation.
Build and maintain a realistic, high-fidelity data application using QueryData. Use this environment as a primary testbed to support ongoing Product R&D, with a specific focus on advancing the Context Engineering Agent.