[Hiring] AI Tooling Engineer @Zyte

Role Description

This role sits in Zyte's Tooling team, working side by side with our Principal Engineer in Tooling on an internal agentic platform: one place that brings together what the company knows, today spread across many systems, so that people and their AI agents get that context in one place, each seeing only what they are allowed to see.

We are looking for a builder: someone who has built AI agents and shipped them to real users, and who likes owning a product end to end, from talking with the people who use it to the code, the deployment and the numbers that say whether it works. The first version is already in use inside the company and grows every week.

• Build the platform with the Principal Engineer in Tooling: new capabilities every week, from design to production.
• Connect new internal systems and SaaS tools to the platform: their APIs and data, how they fit a shared model, and who may see what.
• Build agents and the tools they use, including MCP servers that agents across the company can call, and the context they need to answer well.
• Own quality: evaluation sets and benchmarks, tracing, cost per answer, and fixing what users run into.
• Turn proofs of concept into internal tools that run in production in the cloud, with security and access done properly.
• Build with AI coding agents as the main way of working, with the specs, tests and reviews that keep the code sound.
• Contribute to company-wide AI tooling initiatives: evaluating and standardising internal AI tools, cost and usage tracking, and agreed standards for AI-built internal apps; help other teams build on the platform.
• Talk with the people who use the platform, across the company, to learn what to build next, and explain what was built to technical and non-technical audiences.

Qualifications

• 3+ years building and shipping production software, mostly in Python, with relational databases.
• Has built and shipped with LangGraph at least one agent that real people use: tool calling, retrieval and embeddings, prompt and context design.
• Hands-on experience with LLM APIs from the main providers, cost and token optimisation.
• Hands-on with MCP or similar tool protocols, as a server and as a client.
• Evaluates AI systems properly: test sets, LLM-as-judge, regressions when a prompt or a model changes; tracing and observability with Langfuse or similar.
• Integrates APIs and data: SaaS APIs, SQL, data warehouses.
• Experience with cloud infrastructure (Google Cloud and/or AWS): containers, managed databases, identity and access, CI/CD.
• Uses AI coding agents every day and knows how to keep their output in check.
• A sense for security and permissions: identity, least privilege, people see only what they are allowed to see.
• Comfortable with version control (GitHub/Bitbucket).
• Excellent written and spoken English, technical and non-technical.

Desired Skills

• Familiarity with web data or web scraping.
• Django and Postgres, including vector search with pgvector.
• Enough frontend to build and change a chat interface (React, Next.js).
• Has built internal tools that colleagues adopted.
• Semantic layers or knowledge graphs.
• Memory for agents, multi-agent setups.
• Previous remote/distributed team experience.

Benefits

• Become part of a self-motivated, progressive, multi-cultural team.
• Freedom and flexibility to work remotely.
• Work with cutting-edge, open-source technologies and tools — and shape the AI tooling the rest of the company relies on.