Supply BPO x AI Specialist

The Role in One Sentence

You design and deploy the next generation of Veepee’s procurement processes, combining artificial intelligence to explore business problems with classical technical solutions (rules, deterministic automation) to industrialise them in an economically sustainable way.

Your Responsibilities

The role is structured around 3 areas of responsibility. You are free to choose how you approach them.

Mission 1: Performance and Evolution of Procurement Processes

You are the business owner for the group’s procurement processes. You are accountable for their operational performance, their coherence with other group processes, and their evolution in line with the AI-first ambition.

• Ensure the consistency and operational performance of procurement processes, you decide what deserves attention, you prioritise according to your own criteria, and you own the trade-off decisions

• Identify redesign opportunities and own the target vision, distinguishing what calls for incremental optimisation from what requires genuine transformation

• Ensure alignment with other group functions (commercial, warehouses, finance, scheduling) — you define the right moments and formats for coordination

• Address upstream data quality issues that degrade procurement processes, understand root causes and drive corrections, either directly or through the right stakeholders

• Build the performance view you consider appropriate (KPIs, dashboards, friction points) and communicate it to teams and leadership

Mission 2: Designing AI and Technical Solutions Supporting These Processes

You are responsible for outcomes: solutions that genuinely work, are adopted by teams, and hold up over time — economically and technically. The path to get there is yours.

• Identify the right solutions by exploiting the full range of available tools — from generative AI to explore, to classical technical components to industrialise — based on what the problem requires

• Build yourself when it is most efficient: prototypes, demonstrators, proof-of-value. Engage Tech teams when industrialisation justifies it

• Own architecture decisions, what stays as a generic LLM, what becomes a business rule, what moves to stable ML, what is bought rather than built, and argue them to Tech

• Ensure economic control of solutions: usage cost, latency, human escalation rate, total operational cost. These indicators are at the same level as business quality

• Co-pilot with VeepeeTech teams the solution lifecycle, spec, development, deployment, evolution. You are the reference business counterpart

• Ensure sustainability of what is deployed, documentation, transferability, capacity for future evolution

Mission 3: Team Enablement and Contribution to Group Strategy

A solution that works technically but is not adopted has no value. A transformation vision that does not translate into concrete trajectories for teams remains a slide deck. You own both.

• Support procurement teams in adopting new processes and tools, beyond the operating instructions, build understanding of the underlying logic and create genuine buy-in

• Contribute to the evolution of roles within the procurement team, which positions will change, which skills need developing, how to support people over time

• Build and carry the operational and technical feasibility voice in strategic trade-offs within the Supply & Planning Group

• Actively participate in defining the group’s strategic plan for the procurement function and translating it into concrete initiatives

• Represent the function in cross-functional governance bodies where it matters, you decide your level of engagement based on the stakes

Ways of Working

Veepee is building its operating model AI-first. This role is at the heart of that transformation for the procurement perimeter. Three principles structure day-to-day work:

1. POC with generative AI, industrialise with the right tool

For every operational problem, start with a generative AI prototype, to explore the problem quickly, validate value, and understand real-world patterns. Once the solution is validated, replace the stable and predictable parts with business rules, trained classical ML, lighter LLMs, or deterministic automation. Generative AI remains where linguistic variability or reasoning complexity justifies it.

2. Measure before industrialising

Every deployed solution has its eval dataset, a set of real cases that allows continuous quality measurement. No deployment without a measured baseline. No industrialisation without a quality threshold met.

3. Economic cost is part of the design

A solution that works but costs 10× the business benefit is not a solution. You participate in the trade-off between quality, cost, and latency. You know when a premium LLM is justified, when a light LLM suffices, when a deterministic rule beats any LLM or other AI technology, and when make-or-buy is more relevant.

The Profile We Are Looking For

This role requires a rare combination of operational maturity, technical curiosity, and economic pragmatism. Several career paths can lead here — we are not looking for a specific background, but a combination of capabilities.

Candidate pools we consider

• Consultants or managers in supply chain who have developed a genuine AI practice (not just exposure to the topic)

• Product Managers / Owners who have led products with an AI component and have a strong drive to solve concrete business problems

• Profiles with business experience in supply chain who have led AI transformation projects (in a tech-oriented company with a physical goods supply chain)

• Product Managers / Owners who have led products with an AI component and have a strong drive to solve concrete business problems

• Solutions Engineers or AI Engineers with a strong appetite for supply chain business and customer/business experience

Supply chain business expertise:

• At least 5 years of experience in a supply chain / operations environment, ideally with exposure to procurement challenges

• End-to-end understanding: demand planning, supply planning, logistics, transport, link with commercial functions

• Practice of continuous improvement, process mapping, operational analysis methods

Project and transformation expertise:

• Proven project management skills

• Confirmed aptitude in process optimisation

• Comfortable with Information Systems and agile methods

• Ability to engage with Product Owners and developers — understanding technical constraints

• Experience with product management and quality functional specification (where relevant)

AI in practice:

• Concrete experience prototyping with LLMs, beyond conversational prompts

• Understanding of agentic architectures (function calling, RAG, structured memory), knowing when to use what

• Practice of vibe coding and rapid prototyping tools

• Knowledge of LLM economic constraints: orders of magnitude for per-token costs, latencies, production constraints

• Practice of eval datasets and quality measurement, knowing how to prove an AI solution actually works

Mindset and behaviours:

• Pragmatism: prefers an imperfect POC today over a perfect specification in 6 months

• Economic pragmatism: naturally integrates cost into all technical decisions

• Ability to push without breaking: can carry a transformation vision while respecting teams in place and their trajectories

• Clear written and oral communication, ability to make technical topics accessible to business audiences and vice versa

• Continuous curiosity: actively follows the evolution of AI tools and associated engineering practices

Education and Languages

• Master’s level degree (engineering school, quantitative business school, or university equivalent)

• Fluent French and English, written and spoken (frequent exchanges with Tech teams and other divisions, group internationalisation)

• Another language (Spanish, German, Italian) is a plus but not required

Recruitment Process

After a first screen with the recruiter

• Step 1: Initial conversation (45 min) with the hiring manager to understand your motivations and present the context

• Step 2: Practical case study (1 week to prepare, 1h presentation)  a real operational problem, how you would approach it with a mix of AI and technology

• Step 3: Cross-functional interviews (2h)  with Supply & Planning leadership, a VeepeeTech PO, and a tech peer

• Step 4: Final conversation (1h)  with the N+2 on long-term vision and offer terms