AI Builder – Supply Management
Job Location:
Monthly Salary:
Posted:
Application Deadline:
Vacancies:
Job Summary
On-site services such as catering cleaning maintenance and facilities management.
Benefits and rewards services including meal cards gift cards mobility solutions and employee organisation focuses on improving quality of life for employees students patients and others across sectors such as corporate offices education healthcare defense and remote sites.
Purpose of the Job State concisely the aim of the job
- Explore build and embed practical AI and automation solutions that deliver value to Global Supply Management including its analytics capability.
- Translate real business and operational challenges into working solutions across three practical spaces: the back-end data user facing products and everyday admin.
- Rapidly test and prove ideas in short cycles (weeks not months) assess the value they create and determine whether they should be scaled with IS&T teams reused as lightweight solutions or stopped.
- Act as a hands-on catalyst for responsible AI adoption building solutions rather than simply advising while sharing learning and raising AI capability across the global function.
- The focus is practical value: faster impact delivery better quality stronger adoption and clearer steer on where AI can genuinely help.
Context and main issues
This is a deliberately new and exploratory role.
GSM has a small but growing portfolio of AI opportunities but moving ideas into scaled IS&T led delivery too earlycan extend timelines increase costs and cause opportunities to be missed.
This role provides a faster route to prototype test and prove value before involving IS&T for scaling helping GSMidentify where AI genuinely earns a place and shape the opportunity as it matures.
Organisational Context of GSM & IS&T
GSM operates across global regional and technical teams with different processes data maturity and local needs.
As the BI team moves to a leaner model the challenge is to create reusable scalable value without adding anotherlayer of fragmented solutions.
Many of the best opportunities will not arrive as clearly defined AI use cases. Stakeholders may be time-poorsceptical or struggle to articulate where AI could help so the role must actively uncover pain points challengeexisting ways of working and turn loosely defined needs into practical opportunities to test.
Ownership of core data pipelines sits with IS&T while GSM and BI retain responsibility for the business meaning ofthe data analytics requirements product experience quality and adoption. The role must work effectivelyacross that boundary to accelerate delivery without compromising ownership or standards.
This role will be supported by the IS&T Global Acceleration Team (upskilling AI community technical support).
Example Opportunities to bring it to life
Opportunity exists across the back-end data user facing products and everyday admin that slows teams downincluding ways to make the BI team itself more efficient and effective. Existing data and models can be complex oropaque creating further opportunities for AI to make incremental improvements in how work is simplifiedautomated and delivered.
For instance AI may help accelerate BI projects in areas such as planning scoping cleaning data engineering testing documentation & user training but it does not remove the need for sound data design or human judgement.
For buyers it may help them identify & model potential opportunities more easily. For CSR it may help deliver complex compliance reporting faster.
Building Trust & Value
AI can accelerate work but it is not automatically the right answer. Outputs can be confidently wrong so solutionsmust be grounded in trusted data appropriate validation clear ownership and approved security data and AIgovernance.
Some ideas will remain lightweight or one-off; others will justify wider industrialisation. The role holder must be ableto test quickly prove value then make a clear call: scale reuse partner with IS&T / central AI teams or stop.
Main assignments
AI use case discovery
Lead rapid discovery with GSM BI and regional teams to identify high-value opportunities where AI or automationcould materially improve how work gets done.
Analyse processes pain points and recurring effort to uncover opportunities that stakeholders may not initiallyrecognise or be able to articulate.
Translate business needs into clear testable use cases assessing potential value feasibility risk and speed.
Maintain a prioritised pipeline of opportunities aligned to GSM priorities and expected business outcomes.
Rapid prototyping
Work in short cycles to turn prioritised use cases into working prototypes/MVPs that demonstrate the solution inpractice and allow value to be tested quickly.
Work hands-on across data AI automation and supporting technologies to build an end-to-end solution drawingon existing tools and components where this is faster or more effective. Leverage low-code / no-code and AI-nativetools where appropriate.
Work within the approved technology stack and comply with applicable data-handling security and governancerequirements
Select the approach and technology that best fits the problem balancing speed simplicity reuse cost and thepotential to scale.
Progress ideas far enough to establish whether they are technically viable useful to users and worth furtherinvestment.
User testing iteration and adoption
Put solutions in front of real users early gathering feedback on usefulness accuracy usability and trust.
Improve solutions rapidly based on evidence usage and changing business needs.
Work with stakeholders to build understanding and adoption helping users recognise where AI can enhance theirwork and where it cannot.
Capture lessons from successful/unsuccessful tests so that learning is reused rather than repeatedly value and determine what happens next
Define simple success measures for priority use cases such as time saved quality adoption user experience orbusiness impact and validate outcomes through real-world use rather than technical completion alone.
Test outputs and assumptions appropriately maintaining clear human ownership of decisions and recognisingwhere AI is not sufficiently reliable or valuable.
Make clear recommendations on whether an idea should be scaled reused as a lightweight solution developedfurther retained for a specific need or stopped.
Focus further investment and formal technology involvement on opportunities with demonstrated value and acredible case to progress.
Scale responsibly and build lasting capability
Prepare successful use cases for wider deployment with clear requirements documentation ownership andconsideration of architecture security data governance and ongoing support.
Partner with IS&T central AI teams and other relevant specialists to industrialise solutions where appropriateavoiding unnecessary duplication or unsupported shadow solutions.
Act as a two-way connector between GSM regional innovation and our IT/AI networks translating business needsinto requirements that technical teams can act on and bringing relevant innovation back into GSM.
Create and share reusable solutions components templates patterns and lessons so successful work can beadapted across teams and regions rather than repeatedly rebuilt.
Build AI capability across GSM and BI through practical demonstrations coaching and knowledge sharing whilecontributing to wider AI communities and collective learning across Sodexo.
Person Specification
Essential experience and capability
A hands-on builder who is comfortable working across business analytics and technology and can take an ideafrom a loosely defined problem through to a working solution.
Typically 3 to 5 years relevant experience across analytics data automation digital products or applied AI witha strong track record of building and improving practical solutions.
Strong commercial and business judgement with the ability to understand what matters to stakeholdersrecognise where value can be created and focus technical effort on solutions that are practical useful and worth the investment.
Practical experience using Generative AI and LLM-based tools to solve real business problems with a goodunderstanding of their capabilities limitations and appropriate use is a plus.
Strong data and analytical foundations including experience working with messy or complex data data qualityissues data models and multiple information sources.
Working capability in Python and SQL or equivalent technologies sufficient to manipulate data automate tasksand build prototypes independently.
Ability to combine AI data automation APIs and low-code/no-code tools to create working solutions selectingthe simplest effective approach for the problem.
Strong problem-framing and analytical skills with the ability to uncover the real need behind an initial requestturn ambiguity into something testable and assess value feasibility risk and potential to scale.
Strong user focus with the ability to test solutions in practice respond to feedback and improve usability accuracyand adoption.
Sound judgement around testing data handling security governance and responsible AI including the confidenceto challenge unreliable outputs or conclude that AI is not the right solution.
Able to work with a high level of autonomy move quickly through experimentation and adapt as both thetechnology and the role evolve.
Strong communicator and facilitator who can explain AI in straightforward business language engage sceptical ortime-poor stakeholders and share knowledge across business and technical teams.
Fluent English.
Personal attributes
Curious and pragmatic: interested in what is possible but focused on what genuinely creates value.
Comfortable with ambiguity: willing to explore problems where neither the use case nor the answer is obvious atthe outset. This is a greenfield role so the individual will be expected to bring expertise initiative and direction as therole develops.
Outcome-focused: values working solutions and measurable impact over technical complexity or AI for its ownsake.
Confident and constructively challenging: able to influence stakeholders question existing approaches and pushback where value is unclear.
Collaborative: comfortable operating across GSM BI regional teams and IS&T.
Continuous learner: actively keeps pace with a rapidly changing AI landscape and turns relevant developments intopractical opportunities.
Helpful but not essential
Tech stack: Experience with Power BI Microsoft Fabric Power Platform Copilot Studio Azure or similar enterprisedata and AI platforms.
Analyst: Experience with business intelligence analytics products self-service reporting or user-facing datasolutions.
Build: Experience taking prototypes through governance and into an IT-supported production environment.
International: Experience working in a global or multi-country organisation.
Business: Knowledge of Supply Management procurement or another complex enterprise function.
Language: Additional language capability particularly French
About Company
The best tech recruitment agency in Portugal! We find & deliver the best tech talent with a focus on amazing recruitment experiences.
