Intelligent Automation Engineer

As Intelligent Automation Engineer, you design, build and operate AI-enabled automation and data solutions to transform how information is processed and reviewed. You will deliver end-to-end intelligent workflows, document processing, and analytics that improve quality, speed and decision-making across complex processes. The role supports a major multi-year transformation, shaping scalable capabilities and governance while enabling large-scale review and remediation programmes. You will work with cross-functional teams to integrate AI, data engineering, and workflow technologies, delivering measurable business value and strong traceability.

• Design secure, reliable AI-enabled solutions and intelligent workflows that orchestrate tasks across enterprise systems
• Develop AI-powered knowledge and summarisation tools with governance controls
• Create intelligent document processing solutions using OCR and data extraction
• Build AI-assisted review, remediation and analysis workflows for large-scale operations
• Analyse large datasets to identify cohorts, trends and management information
• Develop data pipelines for cleansing, enriching and transforming data
• Design end-to-end workflows with rules, human review and approvals
• Integrate automation with internal and third-party systems via APIs and event-driven patterns
• Instrument solutions for monitoring, auditing, and operational support
• Apply governance, risk and compliance, including data protection and audit trails
• Collaborate with business stakeholders to optimise processes with AI and automation
• Drive continuous improvement and stay current with AI/automation capabilities

• Strong problem-solving and outcome-focused approach
• Ability to work in ambiguity and deliver value quickly
• Excellent communication to both technical and non-technical audiences
• Experience translating complex requirements into scalable AI/data/workflow solutions
• Analytical mindset for pattern recognition and insights from large data
• Understanding of AI, automation and human oversight in regulated environments
• Disciplined engineering practices: testing, documentation, monitoring and secure delivery
• Experience with document-centric processes and large datasets
• Experience supporting large-scale review/remediation initiatives with structured and unstructured data

• problem-solving
• communication
• adaptability
• AI/ML development with large language models and knowledge-based assistants
• Intelligent Document Processing: OCR, classification, information extraction, form recognition
• AI validation and quality assurance: confidence scoring, human-in-the-loop