About this role
Salary: £104,000 - 117,000 per year
Requirements: Strong experience in AI/ML engineering with hands-on exposure to Generative AI use casesExperience building RAG applications in enterprise environmentsStrong knowledge of document parsing, OCR, and image-based data extractionExperience with LLM orchestration frameworks and prompt designExperience with vector databases and semantic searchVery strong programming skills in PythonExperience working with SQL/NoSQL databases and enterprise data integration patternsUnderstanding of evaluation frameworks for GenAI systems using benchmark datasets and ground-truth-based validationExperience building scalable APIs/services and production-grade AI workflowsExperience with Azure-based AI stack is preferredExperience with high-volume document processing is preferredFamiliarity with enterprise architecture, security, and compliance controls is preferredExposure to monitoring, model evaluation, and AI observability tools is preferredAbility to independently build and deploy GenAI applications from ingestion to retrieval and evaluationStrong problem-solving skills with a practical implementation mindsetComfortable working across data engineering, AI engineering, and application integration Responsibilities: Design and build GenAI/RAG-based applications using data from multiple enterprise systemsIntegrate with structured and unstructured data sources, including databases, APIs, files, and document repositoriesDevelop pipelines to parse and extract data from documents and images using OCR, document intelligence, and related toolsProcess and structure extracted content for downstream AI use casesStore parsed and chunked content in a vector database and manage embeddings effectivelyImplement and optimize retrieval pipelines, including chunking, indexing, metadata tagging, filtering, and rerankingBuild workflows to interact with relational and enterprise databases for querying and enrichmentEnsure the application follows strong evaluation practices, including accuracy, groundedness, relevance, hallucination checks, and response quality against ground truthWork closely with architects, platform teams, and business stakeholders to deliver scalable and secure solutionsFollow enterprise standards for security, governance, observability, and performance Technologies: AIAzureLLMNoSQLPythonRAGSQLSecurityCloud More:
We are seeking a GenAI Engineer to help us build end-to-end AI applications across enterprise data ecosystems. In this role, we work closely with architects, platform teams, and business stakeholders to deliver scalable, secure, and production-ready solutions. We operate within enterprise standards for security, governance, observability, and performance, and we value practical problem-solving and strong implementation skills.
last updated 30 week of 2026