AI Engineer

We are building a dedicated AI Function to deliver intelligent capabilities across our product portfolio. This role sits within that function, working closely with the product team in Budapest.

We are looking for an AI Engineer who wants to own it: someone who will build, maintain, and continuously improve a retrieval-augmented generation (RAG) system from first principles, not assemble one by wiring documents into an off-the-shelf API and hoping for the best

Core focus: retrieval engineering

• Build, maintain, and optimise a RAG system from scratch, covering ingestion, chunking, embedding, indexing, and retrieval, to deliver accurate, trustworthy recommendations to customers
• Implement hybrid retrieval: combining semantic search, metadata and keyword filtering, and reranking across multi-stage retrieval pipelines
• Build pipelines that extract clean, structured metadata from messy source data, for example SAP transport request descriptions, to improve matching and retrieval accuracy
• Build evaluation harnesses that measure retrieval precision against ground truth, and use that data to continuously improve retrieval quality
• Domain validation and continuous improvement
• Design domain validation logic that critically assesses whether retrieved content is genuinely relevant to a customer's specific context, not just semantically similar or superficially plausible
• Design a feedback workflow that allows reviewers to flag and correct retrieval issues, feeding continuously back into the knowledge pool and validation logic

Wider AI platform contribution

• Apply prompt engineering, context engineering, and structured prompt architecture to power AI-driven chatbots and recommendation workflows
• Work with vector database infrastructure to support large-scale semantic retrieval
• Contribute to the centralised evaluation framework used across our AI capabilities, with a focus on the retrieval evaluation component
• Collaborate closely with the Architecture team and the wider AI Function across Budapest and Bracknell

Essential Experience And Skills

• Hands-on experience in AI engineering, building production LLM-powered systems, for example AI-powered chatbots or retrieval-based recommendation tools
• Deep, practical experience building RAG systems from scratch, including chunking strategy, embedding model selection, and indexing design
• Solid understanding of hybrid retrieval: semantic search, reranking, and multi-stage retrieval pipelines
• Experience with vector databases for semantic retrieval at scale
• Experience building evaluation processes that measure retrieval quality against ground truth
• Strong prompt engineering and context engineering skills
• Strong software engineering fundamentals: Python, REST APIs, CI/CD pipelines
• Comfortable working in a fast-moving scale-up environment, figuring things out as you go

Benefits Include

• Competitive salary and annual company bonus, awarded in recognition of overall business performance
• Annual leave in line with Hungarian Labour Law, with entitlement increasing over time based on tenure and other qualifying criteria
• Monthly tax-advantaged SZEP Card contribution (16,700 HUF), usable for accommodation and travel, hot meals and restaurants, cultural programmes, and sports and recreation (available from the month following successful completion of probation)
• Tax-free monthly contribution toward public transport costs
• Monthly cafeteria contribution, taxed at a preferential rate
• Enhanced Parental Leave
• Global Flexibility - work from anywhere for up to one month per year
• Learning and Development Budget
• One paid Volunteering Day per year
• Employee Referral Bonus
• Access to 10 qualified Mental Health First Aiders, plus annual MHFA training opportunities