AI Delivery Enablement Lead

Atos is hiring an AI Delivery Enablement Lead to own the institutional memory, intake, costing, and enablement layer of the Belgium AI delivery practice.

Key Responsibilities

• Create and maintain the AI knowledge base, including ADRs, pattern playbooks, prompt libraries, evaluation-failure catalogues, decision logs, and searchable indexing.
• Run structured AI project intake, capturing scope, risk class, budget, model choice, and engagement gate.
• Maintain planning governance, including templates, dependency maps, and gate-review cadence.
• Build and maintain AI costing and financial models covering token economics, inference cost, training cost, GPU spend, license fees, and FinOps controls.
• Develop the AI case-study library and capture architecture, cost profile, evaluation evidence, and lessons learned from shipped engagements.
• Upskill delivery teams through onboarding paths, workshops, pairing sessions, and office hours.
• Define, track, and report AI KPIs related to adoption, reuse, gate coverage, time-to-productive-first-PR, project-level margin contribution, and risk-register posture.
• Partner with the AI Governance and Platform Lead and the Lead AI Engineer to keep the knowledge base aligned with current governance, model, and data realities.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Information Management, or a related field, or equivalent senior experience.
• 5+ years in a role combining technical depth with documentation, enablement, or programme management.
• 2+ years in or adjacent to AI, ML, or data engineering.
• Strong technical writing skills and experience building knowledge bases, internal wikis, or developer documentation.
• Experience with project intake or governance processes for technical work.
• Comfort with financial modelling, cost build-ups, sensitivity analysis, and scenario modelling.
• Strong data analysis skills using SQL, spreadsheets, and at least one BI tool. Python for analysis is a plus.
• Strong facilitation skills for workshops and enablement sessions.

Preferred Qualifications

• Experience with FinOps for AI workloads, including token economics, semantic caching, model right-sizing, and batch-versus-real-time trade-offs.
• Familiarity with EU AI Act, ISO/IEC 42001, DORA, NIS2, and GDPR documentation requirements.
• Experience with adult learning design, case-study or pattern libraries, and documentation-as-code tooling such as Docusaurus, MkDocs, Backstage, or Notion-as-system-of-record.
• Public writing or talks on AI delivery, knowledge management, or engineering enablement.

Technical Skills

• Knowledge base and documentation tools: Confluence, Notion, Backstage, Docusaurus, MkDocs, or similar, including search and retrieval design.
• Project and governance tools: Jira, Asana, Linear, or equivalent, including stage-gate and gate-review processes.
• Financial modelling and AI FinOps: advanced Excel or Google Sheets, AI cost modelling, token-cost calculators, and FinOps-for-AI principles.
• Data and analytics: SQL, spreadsheets, and at least one BI tool such as Power BI, Looker, Tableau, or Metabase; Python for analysis is a plus.
• AI and ML literacy: understanding of LLM application architecture, RAG, agent platforms, and evaluation.
• Enablement tools: learning platforms, workshop design tools, and scalable pairing models.

Atos embraces diversity and works to create a supportive culture. Tech for good sits at the core of its identity, including efforts related to climate change, digital inclusion, and trust in data management.