Global Solutions Architect AI Factory

The Solution Architect serves as the technical design authority and core builder for the Akkodis AI Factorya global capability dedicated to building industrializing and scaling AI assets. The role operates across a dual focus: making internal AI assets shareable and production-ready across Akkodis and directly advising and delivering for external clients across varying technical ecosystems.

Responsible for architecting reference implementations writing shared accelerators/templates setting global architecture standards and guiding multi-cloud and multi-framework choices across diverse regions and client opportunities

Main responsibilities:

Build & Industrialize Shared Assets:
Prototype architect and build end-to-end reference implementations for strategic AI assets.
Write maintain and package shared code frameworks accelerators and templates that country builders fork from to scale internal asset reusability across Akkodis.
Build foundational tooling and agents to optimize internal delivery processes across Akkodis projects.

Architect Standardize & Govern:
Define and maintain reference architectures for AI assets establishing global standards across all country practices.
Own platform routing and stack selection (low-code vs. pro-code) tailored to multi-tenant multi-country and cost-controlled client environments.
Lead technical design reviews and perform pre-production architectural reviews for key client engagements and internal agents.
Ensure production readiness including packaging versioning deployment and operational handover from PoC to full production.Client Advisory &

Opportunity Support:
Act as a senior technical expert during client engagements leading technical discussions architecture alignment and complex solution design across dynamic client tech stacks.
Design and build client PoCs and demos using AI Factory accelerators abstracting client learnings back into global reusable reference patterns.

Enablement & Community Leadership:
Build and lead a global architecture community driving knowledge sharing and best practice adoption across regional teams.
Mentor regional architects and technical leads on modern data platforms AI patterns MLOps/LLMOps/AIOps and engineering standards.

Main requirements:

Core Architectural & Production Experience:
10 years in data engineering data platform architecture analytics or AI engineering within consulting or enterprise environments.
Proven hands-on experience taking AI solutions to full production with real users (not just POCs).
Proven track record of designing and building reusable assets accelerators and internal platforms.

Technical & Agentic AI Stack:
Internal Tech Stack (Mandatory Minimum): High proficiency in the Microsoft stack (Azure Azure AI Foundry) Python as a primary language and Microsoft Agent Framework (MAF).
External & Multi-Cloud Versatility: Hands-on experience across several cloud environments (AWS or GCP alongside Azure) to adapt seamlessly to varying client stacks and opportunities.
Agent Frameworks & Engineering: Strong experience across several agentic frameworks (including MAF) Model Context Protocol (MCP) orchestration patterns retrieval strategies (e.g. Azure AI Search chunking/embedding hybrid search) and evaluation/monitoring frameworks.
Engineering Fundamentals: CI/CD pipelines Azure DevOps containers GitHub and AI governance principles.