Data engineer for AI platforms

AI Platform is one of our most forward-looking teams. It owns the shared infrastructure and platform layer that AI work across the company runs on — everything from how LLMs are used and measured to the knowledge and retrieval systems built on top of them. A large share of the work is data engineering, because there's a lot of information to move, process and make sense of: LLM usage and cost analytics (tokens, cache, spend), and applied systems like contextual knowledge bases and vector search. Playrix works with roughly 7 petabytes of data.

• Building and owning data pipelines and the platform layer that AI products across the company depend on
• Analytics on LLM usage — cost, token and cache economics, financial reporting on AI spend
• RAG systems: contextual knowledge bases, vector and semantic search infrastructure
• Making the platform operable — profiling, monitoring, observability of the pipelines you own
• Building interfaces and services on top of the data, not just landing it (auth, APIs, interactive access for consumers)

• Strong commercial data engineering experience with a modern stack — e.g. Databricks, dbt, Airflow, Dagster, Greenplum, Snowflake, or similar
• Confident Python, plus a working backend mindset: how authorisation works, how a service is exposed and consumed.
• We're looking for a data engineer who is comfortable building something interactive, not only delivering datasets downstream
• Practical understanding of how to profile and monitor data systems in production
• Real understanding of the AI/LLM layer — vector databases, embeddings, contextual and semantic search, RAG architectures, how autonomous agents work. This is an AI Platform team: we expect conceptual grasp of these technologies, not just having used a coding assistant
• Cloud experience (AWS ideally)

• The opportunity for continuous development in a team of 500+ professional engineers: we have a huge knowledge base and a mentoring system that allows you to adapt quickly.
• Ability to move between areas and not only within development (Project Management, marketing, etc.).
• Tasks that require you to make safe and effective architectural decisions as well as opportunities to apply interesting programming approaches.
• An emphasis on developing each specialist's product ideas
• Time to play the games that you're developing, so you can envision how this or that feature will work for the user.
• The ability to switch out processes and approaches for more efficient ones without lengthy approvals or bureaucracy.