AI Solutions Specialist

Role Overview

We are looking for an AI Solution Specialist to join our team and support the implementation and adoption of AI solutions across the organisation.

The role will be responsible for translating business goals, requirements and challenges into practical AI solutions, working closely with business stakeholders, vendors and internal technology teams to ensure solutions are technically sound, fit for purpose and successfully implemented.

The ideal candidate should have hands-on experience in designing, implementing and deploying AI solutions within an organisation, with a strong understanding of AI technologies, solution architecture, implementation methodologies and AI governance.

This is a delivery-focused role. The successful candidate will not only evaluate and coordinate AI implementations, but should be technically capable of challenging, validating and improving proposed AI solutions, ensuring that they deliver the intended business outcomes and value.

Key Responsibilities

1. AI Solution Design & Implementation

• Translate business requirements and use cases into practical AI solution blueprints and technical specifications.
• Evaluate different AI approaches, tools, models and technologies to determine the most suitable solution.
• Support end-to-end AI implementation lifecycle, from requirements gathering, solution design and development through testing, deployment and post-production support.
• Design, develop, integrate and/or implement AI-powered solutions using appropriate technologies and platforms.
• Ensure AI solutions are scalable, maintainable, secure and fit for the intended business purpose.

1. Technical & Vendor Management

• Act as the techno-functional counterpart to external AI vendors and solution providers.
• Review, assess and validate vendor proposals, solution architecture, technical designs, implementation approach and deliverables, while providing and challenging recommendations where appropriate to ensure solutions are aligned with agreed business requirements and technical specifications.
• Monitor vendor performance, deliverables, milestones and quality of work.
• Work with vendors and internal stakeholders to identify, manage, address and resolve risks, issues, dependencies and gaps.

1. Project Management & Delivery

• Manage and monitor timelines, milestones, deliverables and dependencies, working closely with business stakeholders, IT teams and vendors to coordinate and ensure activities remain on schedule and delivery is within agreed scope, budget and quality.
• Provide regular updates to management and highlight key risks, issues and decisions requiring attention.

1. AI Quality, Governance & Risk

• Ensure AI solutions follow industry best practices.
• Apply suitable methodologies and measures for AI solution evaluation, testing and validation.
• Support the adoption of AI governance, responsible AI use and risk management practices.
• Assess areas such as AI accuracy, reliability, explainability, data quality, security, privacy, safety and potential AI risks.
• Ensure documentation is maintained throughout the implementation lifecycle.

1. AI Enablement & Continuous Improvement

• Identify opportunities to improve existing business processes through AI, automation and emerging technologies.
• Stay current with developments in generative AI, machine learning, LLMs, AI agents, automation and related technologies.
• Evaluate new AI tools and technologies and recommend those that could provide meaningful business value.
• Support the development of internal AI capabilities, knowledge sharing and guidance across the organisation on responsible AI use.

1. Other Responsibilities

• Support other digital transformation, automation and technology initiatives as assigned.
• Undertake other related duties and projects as instructed by Management from time to time.

1. Additional Responsibility

• Perform any other duties, responsibilities or projects as may be assigned or directed by Management from time to time.

Requirements

Essential Requirements

• Degree or diploma in Computer Science, Information Technology, Artificial Intelligence, Data Science, Engineering or a related discipline.
• 3–5 years of relevant experience in AI, technology implementation, solution delivery, digital transformation or a related field.
• Demonstrated experience in successfully implementing at least two AI projects within an organisation, preferably projects that have …