AI Consultant

Overview

We 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 Experience

Generative AI & LLMs

RAG Architecture

Document Intelligence & NLP

AWS & Foundation Models

Vector Databases & Search

Software Engineering

Governance & Regulated Environments

Generative 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.