Atos SE
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.
Position Title: AI Architect Location: Remote Position Type: Full Time Job Description
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., LangGraph, AutoGen, 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, TensorFlow, 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 GitHub 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
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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.
Position Title: AI Architect Location: Remote Position Type: Full Time Job Description
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., LangGraph, AutoGen, 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, TensorFlow, 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 GitHub 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
#J-18808-Ljbffr