AI & Intelligent Automation Technical Lead - UK Export Finance - G7

Location

Westminster, London

About the job

Job summary

The Digital, Data and Technology (DDAT) directorate has recently been established in UKEF, drawing together the expertise in digital currently within the organisation to provide representation of digital services, user centred design, analytics and technology at the highest levels. The areas with the directorate work closely with all areas of the business such as the Business Group, Operations and Strategy and Policy.

UKEF is committed to improving as a customer focussed and led organisation, making it easier for customers to deal with us, offering improved response times, quicker decision-making and improved case-processing. To enable this, UKEF is committed to using digital as a primary means for managing relationships with a wider range of stakeholders. Focusing on developing end-to-end services that meet user need and enable business outcomes, this is an exciting new role showing the growth and drive of UKEFs digital ambition.

The AI & Intelligent Automation Technical Lead will work closely with the AI Product Lead and be supported by a multidisciplinary team including user-centred design (UCD), technology, and delivery professionals to ensure cohesive and impactful AI solutions.

The AI & Intelligent Automation Technical Lead will play a pivotal role in shaping and delivering UKEF’s approach to artificial intelligence, ensuring that AI capabilities are embedded into the organisation’s digital transformation journey. This role will lead the identification, design, and implementation of AI solutions that support strategic objectives, improve operational efficiency, and enhance user experience.

This is a leadership role requiring a blend of strategic thinking, technical expertise, and stakeholder engagement. The AI & Intelligent Automation Technical Lead will be expected to work with a range of technologies and methodologies, adapting to evolving needs and ensuring that UKEF remains at the forefront of responsible AI adoption.

Job description

In this role, you will:

Technical Leadership

• Lead the end-to-end technical design, development, and implementation of AI solutions, including intelligent agents for automation and augmentation.
• Provide technical guidance and mentoring to engineers and analysts working on AI and automation initiatives.
• Document AI architectures, models, and agent behaviours to ensure transparency, governance, and continuous improvement.

Solution Design & Delivery

• Identify opportunities to apply AI agents (task-based, conversational, autonomous) to optimise business processes and improve user experiences.
• Evaluate and implement agent orchestration frameworks and multi-agent systems for complex workflows and adaptive services.
• Integrate AI capabilities with enterprise platforms and services, including low-code environments, APIs, and data pipelines.

Technology Evaluation

• Assess and select appropriate AI models, platforms, and tools (e.g., Azure Foundry, OpenAI, Copilot Studio).
• Stay current with emerging AI technologies, particularly developments in agent-based systems, and evaluate their applicability to UKEF.

Collaboration & Stakeholder Engagement

• Work closely with stakeholders to translate business needs into AI-enabled solutions, incorporating agent-based architectures where appropriate.
• Support the training and upskilling of UKEF staff in AI literacy, responsible use of intelligent systems, and adoption of AI-enabled tools.

Governance & Compliance

• Ensure all AI solutions are ethical, secure, and aligned with UKEF’s strategic objectives and regulatory obligations.

This list is not exhaustive, and you may be required to carry out additional duties according to business needs.

Person specification

Essential

Qualifications

• Graduate-level education or equivalent experience in AI, Data Science, or Software Engineering. (A)
• Evidence of continuing professional development. (A)

Knowledge

• AI and Machine Learning (A, I)
  • Core principles of supervised, unsupervised, and reinforcement learning.
  • Model lifecycle management: training, deployment, monitoring.
  • Ethical and responsible AI practices, including bias mitigation and explainability.
• AI Agents and Agent-Based Systems (A, I)
  • Understanding of task-based, conversational, and autonomous agents.
  • Agent orchestration and coordination in multi-agent systems.

Use of agents to support decision-making, automation, and user interaction.

• Data and Integration (A, I)
  • Data governance and secure handling of structured and unstructured data.
  • Integration of AI solutions with enterprise systems and APIs.

• Cloud and Platform Awareness (A, I)
  • Familiarity with cloud-native AI deployment models.
  • Awareness of platform selection criteria for scalable AI workloads.

• Low-Code and Automation (A, I)
  • Understanding of how low-code tools can support AI adoption.
  • Awareness of hybrid approaches combining low-code and custom AI.

• Security and Compliance (A, I)
  • Knowledge of information security principles in AI contexts.
  • Understanding of data protection regulations (e.g. GDPR).

• Source control of software. (A,I)

Skills/Ability

• Systems Design
  • Design AI systems and agent-based architectures that are scalable, secure, and aligned with business needs.
  • Select and apply appropriate design standards, methods, and tools.
  • Review and guide the designs of others to ensure integration and efficiency.

(Skill level: practitioner) (A, I)

• Information Security
  • Embed security controls in AI and agent-based solutions.
  • Design with threat mitigation and data protection as core features.
• (Skill level: practitioner) (A, I)Agent Design and Orchestration
  • Define agent roles, goals, and interaction protocols.
  • Apply orchestration strategies for multi-agent coordination and task execution.
  • Understand agent lifecycle, memory, and planning capabilities.

(Skill level: working) (A, I)

• Modern Standards Approach
  • Apply appropriate standards and practices for AI development and deployment.
  • Promote maintainability, reusability, and compliance in solution design.

(Skill level: practitioner) (A, I)

• Communication and Stakeholder Engagement
  • Translate complex AI and agent concepts into clear, actionable insights for non-technical audiences.
  • Facilitate solution walkthroughs, demos, and technical discussions.

(Skill level: practitioner) (A, I)

• Leadership and Mentoring 
  • Guide junior engineers and analysts in AI and automation practices.
  • Promote continuous learning and capability building across teams.

(Skill level: working) (A, I)

Experience

• AI Solution Delivery (A, I)
  • Proven experience leading the design, development, and deployment of AI solutions in complex environments.
  • Demonstrated ability to translate business problems into AI-enabled solutions, including the use of intelligent agents for automation, decision support, or user interaction.

• Agent-Based Systems (A, I)
  • Hands-on experience implementing task-based, conversational, or autonomous agents in real-world applications.
  • Experience integrating agents into enterprise workflows or digital services, including orchestration and coordination across systems.

• Platform and Tooling Proficiency (A, I)
  • Experience using modern AI platforms and tools, such as:
  • Azure AI Services and Azure Foundry for scalable model deployment and orchestration.
  • OpenAI APIs (e.g. GPT-5, function calling, embeddings) for generative and conversational AI.
  • Copilot Studio or similar low-code AI tools for rapid prototyping and integration with business workflows.
  • Familiarity with other models and frameworks such as Claude, Gemini, or LLaMA.

• Enterprise Integration (A, I)
• Experience integrating AI models and agents with enterprise platforms (e.g. Dynamics 365, SharePoint, CRM systems) and APIs.
• Ability to deliver hybrid solutions that combine low-code tools with custom AI components.

• Technical Leadership (A, I)
  • Experience leading multidisciplinary teams in the delivery of AI or intelligent automation projects.
  • Ability to mentor and guide engineers, analysts, or data scientists in AI practices and tooling.

• Enterprise Integration (A, I)
  • Experience integrating AI models or agents with enterprise platforms (e.g. CRM, ERP, case management systems) and APIs.
  • Familiarity with hybrid solutions combining low-code tools and custom AI components.

• Ethical and Responsible AI (A, I)
  • Experience applying ethical frameworks, data governance, and risk mitigation strategies in the development and deployment of AI systems.

• Stakeholder Engagement (A, I)
  • Proven ability to engage with senior stakeholders, product owners, and delivery teams to co-design AI solutions that align with strategic goals.
  • Experience communicating complex technical concepts to non-technical audiences.

Qualifications

o Graduate-level education or equivalent demonstrable experience in AI, Data Science, or Software Engineering. (A)
o Evidence of continuing professional development. (A)