AI Enablement Lead

Uni Systems is the leading and most reliable systems integrator in the region, digitally transforming our clients’ businesses across 25 European countries. Within our International Business Unit, we design, build, and operate large-scale, mission-critical Trans-European IT systems for EU institutions and agencies.

We are building AI into the way our Engineering teams deliver software for European institutions and agencies - across architecture, business analysis, development, testing, and project management. This role is how we do it: not through slide decks, but by proving what works on a live client engagement first, then scaling it across the department.

You will be embedded in the Delivery Team of a major EU agency engagement, using AI tools daily on real work under real constraints, and building repeatable workflows that survive contact with a regulated environment. Alongside that, a protected share of your time goes to enablement work across the Engineering Department - capturing what works, sharing it, and building the internal community.

You will lead through influence rather than formal authority. You will design the workflows, templates, and enablement materials, and advise on tooling, spend, and learning paths - but what each team adopts is up to that team and its lead. You will work through the Practice Managers and the practitioners already using AI in their own projects, and you will be supported in doing so.

The role is based in Athens and works on a hybrid basis. You will report to the Head of the Engineering Department and, functionally, to the Delivery Manager for client-facing work. Expect occasional travel to the client site and to EU institutional events.

You do not need to have led an AI transformation before. What matters is that you already use these tools seriously in your own work, are credible in front of engineers and clients, and want to grow with the role as the practice builds around you.

What will you be bringing to the team?

Where you start, and where most of your time sits.

• Deliver as a senior member of the engagement team - hands-on across the SDLC phases relevant to your background.
• Apply AI tooling to real delivery work (Microsoft 365 Copilot, GitHub Copilot, Claude, OpenAI) and build repeatable, production-ready workflows, reusable prompts and SDLC-aligned templates from it - documenting what works and what does not, in a form that transfers to other engagements.
• Establish, with the client and our security and legal functions, what may and may not be processed by hosted AI services under the engagement’s contractual, data-protection and IP constraints - and design workflows that respect those limits, making use of our in-house private AI environment where data cannot leave our control. This is a precondition for everything else, not an afterthought.
• Keep humans accountable for outcomes: AI output is an input to engineering judgement, never a final decision. Set and hold that standard within the team.
• Build the client relationship and act as a credible point of reference for AI questions arising in the engagement.

Running alongside the engagement from the outset, with protected time.

• Act as an internal consultant to delivery teams: understand how each team actually works, advise on where AI can realistically help in their context rather than applying one template across all of them, and turn that into structured recommendations they can act on, with the trade-offs, effort and risks made explicit.
• Build on the internal AI strategic initiative already underway: run hands-on sessions, share working examples, and connect colleagues across architecture, business analysis and engineering who already use these tools in their projects.
• Establish the department’s AI adoption baseline - what tools are in use, by whom, for what, and to what effect - so that later progress can be measured against something real.
• Support the AI Governance Office in operationalising responsible-AI practice within the Engineering Department - transparency, human oversight, traceability, and appropriate use of AI output in deliverables - and help colleagues meet the AI literacy duty under Article 4 of the EU AI Act.
• Track and report AI tooling spend for the department, flag anomalies, and contribute to business cases for new tooling. Budget ownership remains with Finance and the Head of the Engineering Department.

As the practices proven on the engagement mature, your remit broadens accordingly.

• Contribute to the AI adoption roadmap for the Engineering Department with the Practice Managers and the delivery squads and advise on the transition plan for bringing AI tools and methods into existing delivery practice - sequencing, pilots, rollout, training needs, and when to replace or retire what is already in use.
• Guide AI-integrated solution designs and reference architectures; steer PoCs and pilots from experiment towards production use.
• Design role-based learning paths and an upskilling roadmap across the engineering roles; mentor colleagues and build community.
• Support the Bidding team with AI capability statements, technical narratives and differentiators for tenders and proposals; present at client workshops and innovation events.
• Contribute to AI cost governance - allocation across cost centres, quotas and alerts, and optimisation through model selection, prompt efficiency, caching and right-sized compute.

What do you need to succeed in this position?

• University degree in Computer Science, Computer Engineering, Software Engineering, or a related field.
• 10–15 years in IT, with at least 5 years in a senior delivery role - as an Engineer, Solution Architect, or Technical Lead in a services organisation.
• Genuine, daily hands-on use of AI coding and knowledge tools, with concrete examples of workflows you have built for yourself or your team. We will ask you to walk us through them.
• Sound understanding of the full SDLC and where AI does and does not help in each phase.
• Working knowledge of CI/CD, containerisation and cloud-native architectures.
• Working understanding of how AI solutions are put together and judged - prompting patterns, retrieval-augmented generation, model selection and evaluation - enough to guide a design and tell what is feasible from what is not.
• The ability to explain and teach - to make a technique tangible to a colleague who is sceptical of it.
• The ability to turn hands-on experience into advice others can act on, structured input that informs internal decisions and stands up in front of a client - and the presence to deliver it to technical and non-technical audiences.
• Excellent command of the English language (verbal & written).

• Delivery experience in regulated environments, or with EU institutions and agencies.
• Exposure to tenders, proposals or pre-sales work.
• Awareness of cloud cost management and usage-based pricing models.
• Familiarity with the EU AI Act, GDPR, data governance or ISO/IEC 42001.
• Exposure to running LLMs in an environment the organisation controls - private or sovereign deployment, and secure enterprise integration.
• Additional EU languages.

Not entry requirements - what the role will build in you, with support:

• →Depth in the EU AI Act and in applying responsible-AI practice to real delivery.
• →AI FinOps - cost transparency, allocation, and optimisation for AI workloads.
• →Certifications aligned to the role (cloud AI, FinOps, AI governance), funded and supported.