AI Engineering Lead - Banking Sector

Introduction

Our client in the Banking Sector requires the expertise of an AI Engineering Lead to join their team on an initial 12-month contract, subject to the client’s operational requirements.

 

Job Purpose

To provide engineering leadership across Digitization and Automation by owning the technical implementation quality, engineering standards, infrastructure planning, development practices, and engineering governance of DNA solutions.

The AI Engineering Lead works across RPA, Power Platform, Intelligent Solutions, AI, integrations, and custom development initiatives to ensure that approved solution designs are engineered, implemented, deployed, and supported in accordance with enterprise standards.

This is a hands-on leadership role. The incumbent must be capable of designing technical solutions, creating engineering artefacts, building proof-of-concepts and solution skeletons, reviewing code, guiding development teams, and supporting governance forums.

Working alongside the Intelligent Solutions Lead, who owns solution architecture and solution design, the Engineering Lead serves as the DNA engineering authority responsible for how solutions are built, implemented, deployed, secured, and maintained.

Duties & Responsibilities

Engineering Leadership

• Act as the senior engineering authority across DNA.
• Define and maintain engineering standards, frameworks, patterns, and best practices.
• Provide technical leadership across automation, AI, low-code, integration, and software engineering initiatives.
• Serve as the technical escalation point for complex engineering challenges.
• Drive engineering quality, consistency, scalability, security, and maintainability across all DNA solutions.

Engineering Design and Technical Assurance

• Translate solution designs into implementable engineering approaches.
• Produce and review technical designs, infrastructure designs, deployment models, and integration architectures.
• Ensure solutions align to enterprise architecture, security, infrastructure, and operational standards.
• Identify technical risks, dependencies, and implementation challenges early.
• Validate engineering readiness before governance and production deployment.

Governance and Engineering Readiness

• Support GT, architecture, security, infrastructure, and production-readiness forums.
• Ensure engineering artefacts are complete, accurate, and governance ready.
• Present and defend engineering designs when required.
• Review technical assumptions, non-functional requirements, and deployment approaches.
• Ensure that approved solutions can be successfully implemented and supported.

Hands-On Development and Code Review

• Develop proof-of-concepts, technical spikes, and reference implementations.
• Build reusable frameworks, components, and solution skeletons.
• Conduct detailed code reviews across relevant technology stacks.
• Guide developers on coding standards, design patterns, testing, security, and engineering best practices.
• Support teams in resolving complex technical and integration challenges.
• Maintain practical development capability to validate designs and implementation approaches.

Engineering Capability and Continuous Improvement

• Mentor developers, automation specialists, and engineers.
• Promote reusable engineering patterns and assets.
• Drive adoption of DevOps, DevSecOps, and modern engineering practices.
• Improve engineering maturity across all DNA streams.

Desired Experience & Qualification

The successful candidate must demonstrate strong practical engineering capability across multiple technologies, including:

• RPA (Blue Prism or equivalent)
• Power Platform (Power Apps, Power Automate, Copilot Studio)
• .NET and/or Java Development
• API and Integration Development
• Azure Cloud Services
• AI and Intelligent Automation Solutions
• DevOps and CI/CD Practices
• Infrastructure and Deployment Design
• Application Security and Engineering Governance

 

The role requires a hands-on engineer who can both build and review solutions, not solely a manager or architect.

 

Qualifications and Experience

Essential

• Degree in Computer Science, Information Technology, Engineering, or a related field.
• 8+ years' experience in software engineering, automation, systems development, or solution delivery.
• Experience across traditional software development and low code/automation technologies.
• Demonstrated experience in .NET and/or Java development.
• Experience producing technical design and infrastructure artefacts.
• Experience conducting code reviews and providing technical leadership.

Preferred

• Banking or financial-services experience.
• Experience with AI, Azure, and enterprise integration solutions.
• Experience supporting governance and production-readiness processes.
• Relevant Microsoft, Azure, Power Platform, Blue Prism, or architecture certifications.

 

Behavioural Competencies

• Technical Leadership
• Problem Solving
• Strategic Thinking
• Decision Making
• Innovation
• Stakeholder Management
• Coaching and Mentoring
• Collaboration
• Continuous Improvement
• Delivery Focus

 

Key Success Measures

• Improved engineering quality across DNA solutions.
• Improved readiness of solutions submitted to GT and governance forums.
• Reduction in technical defects, rework, and technical debt.
• Increased adoption of engineering standards and reusable patterns.
• Improved developer capability through technical leadership and coaching.
• Delivery of secure, scalable, maintainable, and supportable solutions.

 

Role Positioning within DNA

The Intelligent Solutions Lead owns solution architecture, solution design, technology selection, and overall solution governance.

The AI Engineering Lead owns engineering execution, technical implementation, infrastructure planning, engineering standards, deployment architecture, code quality, technical assurance, and engineering readiness.

Together, these roles provide the end-to-end capability required to take solutions from concept through architecture, engineering, governance, implementation, and production support.

In practical terms, the AI Engineering Lead should be equally comfortable reviewing a Blue Prism design, conducting a Power Apps code review, designing a deployment architecture, building a .NET or Java integration service, creating infrastructure diagrams, or guiding teams through complex engineering challenges. This role effectively serves as the Principal Engineer for the Digitization and Automation function.

Package & Remuneration

Rate per hour

Interested?

Experienced AI Enginnering Leaders with a strong background in artificial intelligence, technical leadership and enterprise technology solutions are invited to submit their CV's for consideration. If you have a passion for driving AI innovation and leading complex engineering initiatives within a banking environment, we would like to hear from you.