AI ARCHITECT

Atos Group is a global leader in digital transformation with c. 67,000 employees and annual revenue of c. €10 billion, operating in 61 countries under two brands — Atos and Eviden. European number one in cybersecurity, cloud and high performance computing, Atos Group is committed to a secure and decarbonized future and provides tailored AI‑powered, end‑to‑end solutions for all industries. Atos Group is the brand under which Atos SE (Societas Europaea) operates.

Atos SE is listed on Euronext Paris.

The purpose of Atos Group is to help design the future of the information space. Its expertise and services support the development of knowledge, education and research in a multicultural approach and contribute to the development of scientific and technological excellence. Across the world, the Group enables its customers and employees, and members of societies at large to live, work and develop sustainably, in a safe and secure information space.

• Experience with fine‑tuning/adapting models (LoRA, RAG, prompt optimization, RLHF basics)
• Strong understanding of LLMs, multimodal models, and transformer architectures
• Ability to design and implement agentic architectures (single‑ and multi‑agent systems)
• Hands‑on experience with agent frameworks (e.g., Lang Graph, Auto Gen, CrewAI, Semantic Kernel, Swarm)
• Skill in defining agent roles, capabilities, tools, and memory patterns
• Experience building autonomous workflows: task decomposition, planning, and self‑correction loops
• Strong prompt engineering skills (system prompts, dynamic context building, tool‑calling protocols)
• Knowledge of grounding strategies to reduce hallucinations and enforce business rules
• Proficiency in Python and common AI/ML libraries (PyTorch, Tensor Flow, OpenAI/Anthropic SDKs)
• Experience building and consuming APIs and microservices for agent tool use
• Familiarity with event‑driven and asynchronous programming patterns
• Experience with RAG pipelines (embeddings, vector stores, retrieval optimisation)
• Knowledge of data engineering fundamentals (ETL, data quality, schema design for knowledge bases)
• Deep experience with cloud platforms (Azure, AWS, GCP) for AI workloads, including:
  • Model hosting and inference optimisation
  • Serverless and container‑based architectures
  • Cost monitoring and scaling strategies
  • Proficiency in cloud‑native deployment architectures (Kubernetes, service meshes, managed inference endpoints)
• Experience deploying agentic systems within Git Hub Enterprise environments, including:
  • Secure secrets management and environment configuration
  • Workflow automation and guardrail integration
  • Compliance with enterprise governance and code review standards
• Ability to instrument and monitor agent behaviour (telemetry, tracing, logs, cost and latency tracking)
• Experience defining and running evaluations for agents (task success, reliability, safety metrics)
• Understanding of security, privacy, and responsible AI principles (PII handling, access controls, auditability)
• Strong debugging and troubleshooting skills for complex, tool‑using agent workflows
• Ability to collaborate with product, data, and engineering teams to translate business needs into agentic solutions
• Clear communication skills for documenting agent designs, assumptions, limitations, and guardrails