AI Delivery Architect
Posted 14 Oct 2026
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 8+ yrs
- Management level
- Individual contributor
No salary listed for this role.
CertificationsISO/IEC 42001 Lead Implementer, AWS Solutions Architect Pro, Azure AI Engineer, GCP Professional ML Engineer, IAPP AIGP
Field of studyComputer Science, Data Science, Engineering (or related)
Study levelBachelor's or higher
Who we are.
We are a team of passionate experts with a clear ambition: applying digital technology to advance what matters for our clients and society.
Together we create reliable and responsive digital foundations for the world’s businesses, institutions, and communities.
Learn more on Advancing what matters
Atos is hiring an AI Delivery Architect to help define and deliver AI architecture patterns, engineering standards, evaluation frameworks, and governance practices across Belgium delivery teams. The role combines strategic architecture ownership with hands-on technical delivery, supporting production AI systems from concept to scale.
Key Responsibilities
- Define and maintain AI delivery reference architectures, pattern libraries, and target-state architectures for AI engagements.
- Establish engineering standards for LLM applications, agent orchestration, prompt management, retrieval pipelines, model serving, observability, and lifecycle management.
- Work directly with delivery teams to translate strategy into shipped artefacts, including Python MCP servers, typed agent boundaries, and evaluation gates in CI.
- Conduct AI risk assessments covering EU AI Act classification, provider-versus-deployer determination, ISO 42001, DORA, NIS2, and GDPR alignment.
- Evaluate AI platforms, model providers, sovereign vendors, agent platforms, evaluation tooling, and open-source components based on fit, cost, performance, sovereignty, and risk.
- Partner with platform, MLOps, and LLMOps teams on CI/CD, infrastructure-as-code, observability, drift detection, incident response, and AI FinOps.
- Drive responsible AI practices, including privacy, auditability, bias mitigation, explainability, human-in-the-loop controls, and technical documentation for high-risk classifications.
Required Qualifications
- Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field, or equivalent senior engineering experience.
- 8+ years in software or enterprise architecture, distributed systems, or AI/ML platform engineering.
- 3+ years of hands-on experience designing and shipping production AI/ML or generative AI systems, including LLM systems taken from prototype to production.
- Strong hands-on engineering skills in Python, MCP servers, typed agent boundaries, evaluation harnesses, and CI integration.
- Production experience with cloud platforms such as AWS, Azure, or Google Cloud, and at least one sovereign-EU stack such as OVHcloud, Mistral, Aleph Alpha, Scaleway, Outscale, or Polaris.