Principal AI/RAG Engineer

Required skills: Python, LLM, RAG About the Project Our client is a leading global investment management company headquartered in London, managing over $228 billion in assets for institutional investors worldwide. Data science, machine learning and AI play a central role in the firm's investment and research processes. As part of this engagement, we are building the foundations that enable safe and scalable AI adoption in highly regulated financial environments. The focus is on creating AI-ready data platforms that allow agents to securely discover, understand and reason over enterprise data. This is an opportunity to work at the intersection of Data Engineering, AI, RAG systems and enterprise-scale information management. Your Role We are looking for an experienced Principal AI/RAG Engineer who combines strong Python engineering skills with hands-on expertise in LLMs, Retrieval-Augmented Generation (RAG), and modern data platforms. You will design and build the catalog, semantic, metadata and analytical layers that transform complex on-premise data estates into AI-accessible systems. You'll play a key role in improving retrieval quality, building evaluation frameworks, developing extraction pipelines and enabling trusted AI-powered experiences. Responsibilities Design and develop production-grade AI and RAG solutions. Build and maintain evaluation frameworks, automated test suites and quality gates for AI systems. Enhance retrieval pipelines using hybrid search, reranking and metadata-driven filtering. Develop document intelligence solutions, including structure-aware parsing, chunking and information extraction. Build metadata and entity extraction pipelines with confidence scoring and human review workflows. Design synchronization mechanisms for enterprise content platforms. Develop document lineage, temporal views and document relationship models. Contribute to knowledge graph and query orchestration capabilities. Create monitoring, observability and quality dashboards for AI services. Ensure provenance, traceability and governance across all generated answers and extracted information. Collaborate directly with client stakeholders in a regulated financial environment. Requirements Must Have 6+ years of commercial Python development experience. 2+ years of hands-on experience building production LLM and RAG systems. Strong understanding of: Retrieval pipelines Vector databases Structured information extraction AI operational tooling Experience with evaluation frameworks such as: Langfuse RAGAS DeepEval or similar solutions Strong expertise in hybrid retrieval techniques: Keyword search Semantic search Cross-encoder reranking Retrieval optimization Experience with document processing and extraction pipelines. Knowledge of OCR-based document processing. Practical experience working with AI agents and agentic workflows. Advanced PostgreSQL knowledge, including: Relational data modeling JSONB Schema migrations Data transformations Strong understanding of data governance, provenance and traceability. Excellent communication skills in English. Nice to Have SharePoint and Microsoft Graph API integrations. Knowledge graph technologies such as Neo4j or Apache AGE. Experience building permission-aware retrieval systems. Experience with MCP tools, AI agents or AI coding assistants. Legal, contract management or document intelligence domain knowledge. Financial services experience. Experience working in regulated enterprise environments. Bitemporal data modeling and document lineage solutions. Cost and token-efficiency optimization for LLM applications. Tech Stack Python LLMs RAG Langfuse / RAGAS / DeepEval PostgreSQL Vector Databases Knowledge Graphs SharePoint Microsoft Graph API OCR Technologies AI Agents Enterprise Search Solutions Why Join? Work on cutting-edge AI and Agentic AI initiatives Influence the architecture of enterprise-scale AI platforms Solve complex challenges related to trusted and secure AI adoption Collaborate directly with an internationally recognized financial institution High level of technical ownership and autonomy Fully remote work model Long-term strategic project with significant business impact If you're passionate about building AI systems that work reliably in real-world enterprise environments, we'd love to hear from you.