About the job
Location: EU
About the Role:
Navigating the fast-paced world of AI tooling can be challenging; turning these tools into practical applications for teams is even more complex. This is where you will make your mark.
In this role, you will collaborate with the VP of Engineering and the Chief Architect to develop and manage essential AI tools that our engineering teams rely on. This includes working with agents, plugins, MCP servers, integrations, and the surrounding infrastructure.
This is not a research position; it focuses on hands-on engineering and enablement.
You will directly influence how our engineering teams approach software development, marking the beginning of a dedicated AI platform team we are aiming to establish over the next year.
Key Responsibilities:
Stay updated on valuable AI tools. Evaluate offerings from Anthropic, OpenAI, Google, and the open-source landscape. Test these innovations, form evidence-based opinions, and provide actionable recommendations.
Transition AI tools from prototype to production. Set up agents, plugins, MCP servers, hooks, and integrations that ensure reliability. Manage operational aspects, including access, cost limits, and audit trails.
Oversee the AI coding assistant stack, installation, configuration, updates, cost monitoring, and proactive problem resolution.
Develop a shared infrastructure, including MCP servers, agent configurations, reusable prompts, and custom tools, all meticulously version-controlled and documented.
Assist teams in leveraging AI tools effectively. Work closely with engineers on practical projects to transition their usage from an occasional trial to a regular part of their workflow.
Collaborate with the Chief Architect to define best practices. Establish code review standards, coding patterns, and tooling that facilitate adherence.
Engage regularly with engineers to identify challenges and streamline processes while sharing effective practices.
Qualifications:
Proven software engineering experience, with successful deployment of production code across various stacks.
Hands-on experience with AI coding agents such as Claude Code, Cursor, Cline, or Copilot, understanding their strengths and limitations.
Practical experience with AI features, including agents, RAG, MCP integrations, and LLM-backed functionalities relied upon by users.
Familiarity with agentic patterns: MCP, tool usage, context management, prompt engineering, and evaluation.
