AI Integration Specialist

Responsibilities:

Continuously improve the AI interaction experience for company employees identifying friction, proposing solutions, and iterating based on real usage data.
Audit existing AI tools and automation workflows across assigned business areas to identify inefficiencies, duplication, risks, and high-value opportunities.
Build and maintain an AI/automation backlog and roadmap, prioritizing initiatives by impact, effort, and risk: each with clear KPIs and a baseline-to-target measurement approach.
Lead 1–2 small-to-mid initiatives end-to-end: from opportunity assessment and PoC through to pilot, rollout support, and post-launch performance tracking.
Contribute to AI governance alongside Security, Compliance, and Legal, applying existing policies and helping maintain practical checklists and standards for data handling, access controls, logging, and vendor usage.
Support the evaluation and onboarding of external AI vendors and partners: collect requirements, coordinate demos and pilots, participate in due diligence, and summarise ROI findings with a clear recommendation.
Align stakeholders and keep delivery moving: facilitate working sessions, document decisions, surface blockers early, and provide transparent status updates without waiting to be asked.
Produce clear, audience-appropriate documentation: requirements (PRD/BRD), decision records (ADRs), rollout runbooks, user guidance, and progress reports.
Analyse routine tasks with business stakeholders, design AI-based automation, run testing and UAT, deliver the final solution, and confirm satisfaction post-launch.

Requirements:

3+ years of proven experience supporting or co-leading cross-functional technology or automation initiatives that delivered measurable business improvements.
Solid working knowledge of AI and LLM concepts, practical use cases, and real limitations, including familiarity with integration approaches such as APIs, workflow automation tools, iPaaS, or basic RPA solutions.
Ability to move initiatives forward through collaboration and clear communication, even without formal authority over the teams involved.
Strong analytical skills: able to define success metrics, set baselines, and measure outcomes across time saved, cost reduction, quality improvements, and risk reduction.
Clear written and verbal communication in English: comfortable translating between business needs and technical implementation details for both audiences.
Hands-on experience with delivery and collaboration tools such as Jira, Confluence, or equivalent.

Nice to have:

Experience working in a regulated industry such as fintech or payments, particularly within strict information security environments.
Hands-on experience with LLM platforms and applied use cases such as retrieval-augmented generation (RAG), automated classification, or AI copilots for business processes.
Background in vendor evaluation, risk assessment, and procurement, including privacy reviews and contractual considerations such as DPAs and SLAs.
A habit of tracking AI trends at a practical level and translating relevant developments into concrete internal improvements.