AI NLP Engineer

Role overview

Responsibilities

• check_circle Own and evolve the core text analytics pipeline; advancing large-scale concept extraction, classification, and information retrieval across complex social and clinical text corpora.
• check_circle Design and implement LLM-based processing chains for structured information extraction, leveraging prompt engineering, output parsing, and model orchestration to produce high-quality, auditable outputs at scale.
• check_circle Build and manage embedding infrastructure; training, fine-tuning, and serving embedding models, and setting up and operating vector databases to enable semantic search and retrieval across client data.
• check_circle Develop and maintain classical NLP components where appropriate; training smaller classifiers, entity recognisers, and domain-specific models for tasks where efficiency and interpretability outweigh generative approaches.
• check_circle Lay the groundwork for agentic AI capabilities as the platform evolves; contributing to the design of multi-agent orchestration, tool integration, and conversational interfaces over Xantura's services.
• check_circle Ensure all NLP systems are robust, explainable, and aligned with Responsible AI principles; essential where outputs inform decisions about vulnerable people in health and social care.
• check_circle Bachelor's or Master's degree in Computer Science, Computational Linguistics, Machine Learning, or a related technical field, or equivalent practical experience.
• check_circle 3+ years of professional experience in an NLP, ML, or AI engineering role
• check_circle Strong programming skills and production experience in Python.
• check_circle LLM utilisation in production; prompt engineering, output structuring, chaining, and integrating LLMs into data processing pipelines (e.g. via LangChain, PydanticAI, or similar);
• check_circle Embedding models; training, fine-tuning, or serving embedding models (e.g. sentence-transformers, bi-encoders, cross-encoders), with practical experience setting up and managing vector databases (e.g. Qdrant, Weaviate, Milvus, pgvector) along with understanding trade-offs e.g. when to use sparse or dense embeddings (or both) etc;
• check_circle Classical NLP training and evaluating text classifiers, NER models, or other supervised/semi-supervised NLP models for domain-specific tasks.
• check_circle Experience with knowledge graphs/triplestores/semantic web frameworks (e.g. Neo4j, RDF/SPARQL/OWL, Apache Jena).
• check_circle Practical experience with entity linking, concept normalisation, or ontology-driven NLP.
• check_circle Experience with retrieval-augmented generation (RAG) pipelines
• check_circle Familiarity with agentic AI frameworks and multi-agent orchestration (e.g. LangGraph, AutoGen).
• check_circle Good familiarity with the Azure ecosystem (Azure Kubernetes Service, Azure Container Registry, Azure DevOps, Azure Blob Storage, Azure Monitor, Azure Key Vault).
• check_circle Competitive salary reviewed annually
• check_circle Work for a passionate, mission-driven company solving society’s big problems
• check_circle Work flexible hours around life commitments with a focus on delivering company value rather than hours worked
• check_circle Ability to work remotely (excluding face-to-face Team Meetings and client meetings)
• check_circle Training and development opportunities
• check_circle 25 days annual leave (plus bank holidays)
• check_circle Company pension
• check_circle Private medical insurance
• check_circle Generous enhanced parental leave policies
• check_circle Cycle to work scheme
• check_circle Flu Vaccinations,
• check_circle Eye Test and contribution towards Glasses for VDU use
• check_circle Mental health and wellbeing support
• check_circle Remote GP access
• check_circle Counselling/therapy
• check_circle Physiotherapy
• check_circle Medical second opinions

About the company