AI Delivery Architect
Atos is hiring an AI Delivery Architect. Level rates it ; you can apply on Atos.
Level's read on this job
- Level rates this role Builds AI ●●●●: the job is building AI systems.
- Tools and skills the posting cites include OpenAI, Claude and Anthropic.
- Level classifies it as a mid-level role.
- Level lists 609 other open roles at Atos.
- The role was posted within the past two weeks.
AI in this role
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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.
- 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.
- Deep knowledge of LLM application architecture, including RAG, function calling, agentic orchestration, fine-tuning, prompt orchestration, and model serving.
- Familiarity with MLOps and LLMOps tooling, model lifecycle management, observability frameworks, and CI/CD for AI systems.
- Working knowledge of EU AI Act risk classification, ISO/IEC 42001, DORA, NIS2, GDPR, enterprise security, IAM, encryption, data protection, and data-residency requirements.
- Ability to communicate architecture decisions clearly to delivery teams and senior stakeholders.
- Experience in regulated industries such as financial services, healthcare, public sector, or critical infrastructure.
- Certifications such as ISO/IEC 42001 Lead Implementer or Lead Auditor, AWS Solutions Architect Pro, Azure AI Engineer, GCP Professional ML Engineer, or IAPP AIGP.
- Familiarity with architecture frameworks such as TOGAF, Zachman, C4, and stage-gate delivery models.
- Experience building internal developer platforms, AI accelerators, reusable enterprise AI asset libraries, or production-quality agent tooling.
- Public writing, open-source contributions, or conference talks related to AI engineering systems.
- AI/ML: LLMs, RAG, embeddings, vector search, model evaluation, fine-tuning, agentic orchestration, and classical ML.
- Agent platforms: Anthropic Claude, Microsoft Copilot Studio and Agent 365, AWS Bedrock AgentCore, Google Vertex AI Agent Builder, and Atos Polaris.
- Architecture and engineering: distributed systems, microservices, event-driven architecture, API design, Python, FastAPI, async, Pydantic, pytest, TypeScript or Go, CI/CD, and infrastructure-as-code.
- Data and platforms: Databricks, Snowflake, vector stores, knowledge graphs, AWS, Azure, GCP, Kubernetes, Docker, serverless, GPU infrastructure, and sovereign stacks.
- Observability, operations, governance, and security: Langfuse, Arize Phoenix, Ragas, DeepEval, Promptfoo, OpenAI Evals, monitoring, tracing, drift detection, AI FinOps, IAM, zero-trust, EU AI Act, ISO 42001, DORA, and responsible-AI guardrails.
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How we rate this
AI Delivery Architect at Atos rates 95 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you decide when an AI agent can act on its own versus asking for approval first?
- Walk me through fine-tuning a model: what data did you use, and how did you check the result?
- How do you monitor a model once it's live, and how do you know it needs retraining?
- How do you decide that one model's output is better than another's for a given task?
Adapt your resume
- List these exact terms on your resume: RAG, AI agents, Fine-tuning, ML Ops, and AI Evaluation. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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