MCP & Tools Python Developer - Agent Evaluation Infrastructure at Mindrift

, British Columbia, Canada -

Full Time

Start Date

Immediate

Expiry Date

20 Mar, 26

Salary

45.0

Posted On

20 Dec, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Backend Development, API Development, Testing Frameworks, Protocol-Based Interfaces, Docker, Linux CLI, HTTP Communication, LLM Agents, FastAPI, Debugging, Documentation, Communication, Model Context Protocol, Scoring Functions, Dev Environments

Industry

technology;Information and Internet

Description

This opportunity is only for candidates currently residing in the specified country. Your location may affect eligibility and rates. Please submit your resume in English and indicate your level of English proficiency. At Mindrift, innovation meets opportunity. We believe in using the power of collective human intelligence to ethically shape the future of AI. What we do The Mindrift platform, launched and powered by Toloka, connects domain experts with cutting-edge AI projects from innovative tech clients. Our mission is to unlock the potential of GenAI by tapping into real-world expertise from across the globe. Who we're looking for Calling all security researchers, engineers, and penetration testers with a strong foundation in problem-solving, offensive security, and AI-related risk assessment. If you thrive on digging into complex systems, uncovering hidden vulnerabilities, and thinking creatively under constraints, join us! We’re looking for someone who can bring a hands-on approach to technical challenges, whether breaking into systems to expose weaknesses or building secure tools and processes. We value contributors with a passion for continuous learning, experimentation, and adaptability. About the project We’re on the hunt for hands-on Python engineers for a new project focused on developing Model Context Protocol (MCP) servers and internal tools for running and evaluating agent behavior. You’ll implement base methods for agent action verification, integrate with internal and client infrastructures, and help fill tooling gaps across the team. What you’ll be doing: Developing and maintaining MCP-compatible evaluation servers Implementing logic to check agent actions against scenario definitions Creating or extending tools that writers and QAs use to test agents Working closely with infrastructure engineers to ensure compatibility Occasionally helping with test writing or debug sessions when needed Although we’re only looking for experts for this current project, contributors with consistent high-quality submissions may receive an invitation for ongoing collaboration across future projects. How to get started: Apply to this post, qualify, and get the chance to contribute to a project aligned with your skills, on your own schedule. Shape the future of AI while building tools that benefit everyone. The ideal contributor will have: 4+ years of Python development experience, ideally in backend or tools Solid experience building APIs, testing frameworks, or protocol-based interfaces Understanding of Docker, Linux CLI, and HTTP-based communication Ability to integrate new tools into existing infrastructures Familiarity with how LLM agents are prompted, executed, and evaluated Clear documentation and communication skills - you’ll work with QA and writers We also value applicants who have: Experience with Model Context Protocol (MCP) or similar structured agent-server interfaces Knowledge of FastAPI or similar async web frameworks Experience working with LLM logs, scoring functions, or sandbox environments Ability to support dev environments (devcontainers, CI configs, linters) JS experience Get paid for your expertise, with rates that can go up to $45/hour depending on your skills, experience, and project needs. Take part in a flexible, remote, freelance project that fits around your primary professional or academic commitments. Participate in an advanced AI project and gain valuable experience to enhance your portfolio. Influence how future AI models understand and communicate in your field of expertise.

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Responsibilities

Develop and maintain MCP-compatible evaluation servers while implementing logic to check agent actions against scenario definitions. Collaborate with infrastructure engineers to ensure compatibility and assist with testing when needed.