Full job description

Senior GenAI / AI Engineering ConsultantContract: Initial 3-Month Contract
Rate: Up to £500 per day (Inside IR35)
Location: Remote with occasional travel to client site
Start Date: ASAP
OverviewWe are supporting a major organisation undertaking a large-scale AI and document intelligence transformation programme. They are seeking an experienced Senior GenAI / AI Engineering Consultant to design and deliver enterprise-grade Generative AI solutions within a highly regulated environment.
This is a hands-on role requiring experience building production-ready GenAI applications rather than proof-of-concept solutions. You will work across LLM architecture, RAG frameworks, document intelligence, vector search, and AWS cloud-native AI services.
Key Responsibilities*Design and implement production-scale GenAI and LLM-powered solutions.
*Build Retrieval Augmented Generation (RAG) architectures for document-heavy use cases.
*Develop document processing pipelines capable of extracting and validating information from complex PDFs, scanned documents, and regulatory records.
*Design multi-agent workflows incorporating extraction, validation, routing, scoring, and human-in-the-loop review processes.
*Implement semantic and hybrid search solutions using Amazon OpenSearch vector search capabilities.
*Establish evaluation frameworks for AI-generated content, including accuracy, precision, recall, confidence scoring, and hallucination detection.
*Work closely with compliance, security, and business stakeholders to ensure governance and regulatory requirements are met.
*Develop production-quality Python applications and support CI/CD deployment pipelines.
Essential ExperienceGenerative AI & LLMs*Proven experience designing and deploying production GenAI solutions.
*Experience building end-to-end LLM pipelines processing real customer documents at scale.
*Strong understanding of hallucination detection, output validation, confidence scoring, and model evaluation.
RAG Architecture*Experience designing Retrieval Augmented Generation solutions in document-intensive environments.
*Knowledge of chunking strategies, embedding model selection, retrieval optimisation, and cross-document reasoning.
*Experience defining and measuring retrieval quality metrics.
Document Intelligence & NLP*Delivery experience extracting structured information from unstructured and semi-structured documentation.
*Experience working with PDF documents, scanned files, regulatory submissions, and compliance documentation.
*Strong understanding of data quality controls and validation frameworks.
AWS & Foundation Models*Hands-on experience with Amazon Bedrock and foundation models such as Claude.
*Experience implementing prompt engineering, guardrails, safety controls, and model benchmarking.
*Strong AWS development experience using Python and Boto3.
Vector Databases & Search*Experience implementing semantic search solutions using Amazon OpenSearch Service.
*Knowledge of:*Vector indexing and embedding storage
*k-NN search configuration
*Hybrid search (keyword + semantic)
*Performance optimisation for large-scale retrieval workloads

Software Engineering*Strong Python development experience within production environments.
*Experience with:*LangChain
*LlamaIndex
*LangGraph or CrewAI
*AWS SDK (Boto3)
*PyPDF
*Amazon Textract
*Unstructured.io

*Experience implementing CI/CD pipelines and deployment automation.
Governance & Regulated Environments*Experience delivering solutions within highly regulated sectors such as financial services.
*Understanding of:*PII handling
*Data classification frameworks
*Audit and traceability requirements
*Information security controls
*Risk and compliance governance