Level

Atos

Lead AI Engineer

Atos is hiring a Lead AI Engineer. Level rates it ; you can apply on Atos.

Level's read on this job

  • On Level's scale, this AI engineer role is Builds AI ●●●●: the job is building AI systems.
  • Tools and skills the posting cites include OpenAI, Hugging Face and Bedrock.
  • Level classifies it as a lead role.
  • Level lists 609 other open roles at Atos.
  • The role was posted within the past two weeks.

AI in this role

openaihugging-facebedrockvllmpytorchsagemakerdatabricksray
ragfine-tuning

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 a Lead AI Engineer to build and operate the model, infrastructure, and data layer of the Belgium AI delivery practice. The role trains and fine-tunes custom large and small language models for project use, builds and administers the AI infrastructure those models run on, and leads the data management, modelling, and integration work that every AI project depends on.

Key Responsibilities
  • Build and train custom LLMs and SLMs. Fine-tune, align, and adapt models for project needs.
  • Create and administer the AI infrastructure. Set up compute, serving, orchestration, autoscaling, and cost controls.
  • Lead data management for AI projects. Build pipelines, vector stores, feature stores, and lineage tracking.
  • Lead modelling decisions across the portfolio. Choose the right approach: foundation model, fine-tune, or custom SLM.
  • Lead AI integration into project codebases. Connect models with project and client systems through APIs, retrieval layers, and agent frameworks.
  • Run the evaluation harness for trained models. Define metrics, test sets, and CI-based quality gates.
  • Partner with the AI Governance and Platform Lead on governance. Provide model cards, data lineage, evaluation results, and prompt documentation.
  • Mentor delivery-team engineers. Support teams on model selection, fine-tuning, and data-quality practices.
Required Qualifications
  • Bachelor’s or Master’s degree in Computer Science, Machine Learning, Engineering, or equivalent experience.
  • 6+ years in ML engineering, deep learning, or AI platform engineering.
  • 3+ years training, fine-tuning, or deploying LLMs in production.
  • Experience with model tuning techniques such as LoRA, QLoRA, instruction tuning, RLHF, or DPO.
  • Knowledge of model-serving runtimes and GPU infrastructure on a major cloud platform.
  • Strong data engineering background across pipelines, vector stores, feature stores, and lineage tracking.
  • Senior-level Python skills with PyTorch and modern AI tooling.
  • Good understanding of evaluation methodology, hallucination measurement, and retrieval failure analysis.
  • Production experience taking at least one significant model from training to live operation.
Preferred Qualifications
  • Experience training small language models or running continued pretraining.
  • Hands-on RAG experience at scale, including chunking, embeddings, reranking, and evaluation.
  • Experience with sovereign EU AI stacks for model training or serving.
  • Hands-on with agent frameworks in production environments.
  • AI FinOps experience covering cost control, right-sizing, caching, and workload optimisation.
  • Open-source contributions, papers, or talks are a plus.
Technical Skills
  • Modelling. PyTorch, Hugging Face, DeepSpeed, Accelerate, vLLM, TGI, LoRA/QLoRA, DPO, and RLHF.
  • Inference and serving. vLLM, TGI, Triton, Ray Serve, SageMaker, AWS Bedrock, autoscaling, and latency/cost optimisation.
  • Data. Databricks, Snowflake, vector stores, feature stores, knowledge graphs, and lineage tools.
  • Cloud and compute. AWS, Azure, GCP, Kubernetes, Docker, GPU infrastructure, and sovereign EU stacks.
  • Evaluation. Langfuse, Arize Phoenix, Ragas, DeepEval, Promptfoo, OpenAI Evals, and custom evaluation harnesses.
  • Languages. Senior-level Python plus TypeScript, Go, or Rust for production integration.
  • Engineering practice. Typed boundaries, testing, CI/CD, and infrastructure-as-code.

 

Learn more about us:

 

At Atos, we embrace diversity as the ultimate engine of ingenuity for our clients, and we constantly strive to create a culture where people feel supported and encouraged. Read more about our commitment here . 

 

Whether it is fighting climate change, promoting digital inclusion, or ensuring trust in data management – tech for good sits at the core of our identity. With numerous global recognitions for our ESG practices, we are committed to building a better future for all by harnessing the power of technology. Learn more here.

 

How we rate this

Lead AI Engineer at Atos rates 100 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.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ 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

RAGFine-tuningOpenAIHugging FaceBedrockvLLMPyTorchSageMaker

Also common in AI engineer jobs at this level

Pay

Typical AI engineer job
$182k

This posting doesn't list pay. Use the typical AI engineer figure when pay comes up. See AI engineer jobs

Questions you could be asked

  1. How would you design a retrieval step so the model answers from real data instead of guessing?
  2. Walk me through fine-tuning a model: what data did you use, and how did you check the result?
  3. What are the limits of OpenAI that you've run into, and how did you work around them?
  4. What's a project where you used Hugging Face hands-on?
  5. Walk me through how you've used Bedrock in your day-to-day work.

Adapt your resume

  • List these exact terms on your resume: RAG, Fine-tuning, OpenAI, Hugging Face, and Bedrock. 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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