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Agentic AI Architect (GCP)

Publication Date:  Jan 14, 2026
Ref. No:  541817
Location: 

Windsor, CA

About Atos Group

 

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 for services and Eviden for products. 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:          Agentic AI Architect (GCP)

Location:                  Remote

Position Type:         Full Time



Job Description:

Responsible for: Lead the design and development of an Agentic AI platform.

Deep expertise in machine learning, system architecture, and AI agent frameworks to build scalable, autonomous systems.

Architect and implement core systems for agent-based AI workflows.

Design and deploy LLM-based pipelines, agent orchestration, and vector-based memory systems.

Develop and optimize ML models, pipelines, and orchestration logic.

Drive technical strategy, tooling, and infrastructure decisions.

Architect and implement agentic AI systems leveraging GCP services (Vertex AI, BigQuery, Cloud Functions, Pub/Sub, etc.).

 

Requirements:

  1. Industry Experience: Several years of industry experience in AI/ML and data engineering, with a track record of working in large-scale programs and solving complex use cases using GCP AI Platform/Vertex AI. ·
  2. Agentic AI Architecture: Exceptional command in Agentic AI architecture, development, testing, and research of both Neural-based & Symbolic agents, using current-generation deployments and next-generation patterns/research. ·
  3. Agentic Systems: Expertise in building agentic systems using techniques including Multi-agent systems, Reinforcement learning, flexible/dynamic workflows, caching/memory management, and concurrent orchestration.
  4. Proficiency in one or more Agentic AI frameworks such as LangGraph, Crew AI, Semantic Kernel, etc.
  5. Python Proficiency:
  6. Expertise in Python language to build large, scalable applications, conduct performance analysis, and tuning.
  7. Prompt Engineering: Strong skills in prompt engineering and its techniques including design, development, and refinement of prompts (zero-shot, few-shot, and chain-of-thought approaches) to maximize accuracy and leverage optimization tools.
  8. IR/RAG Systems: Experience in designing, building, and implementing IR/RAG systems with Vector DB and Knowledge Graph.

 

INTERNAL USE · Model Evaluation: Strong skills in the evaluation of models and their tools. Experience in conducting rigorous A/B testing and performance benchmarking of prompt/LLM variations, using both quantitative metrics and qualitative feedback.

 

Technical Skills Required:

  1. Programming Languages
  2. Proficiency in Python is essential.
  3. Agentic AI
  4. Expertise in LangChain/LangGraph, CrewAI, Semantic Kernel/Autogen and Open AI Agentic SDK
  5. Machine Learning Frameworks
  6. Experience with TensorFlow, PyTorch, Scikit-learn, and AutoML.
  7. Generative AI
  8. Hands-on experience with generative AI models, RAG (Retrieval-Augmented Generation) architecture, and Natural Language Processing (NLP).
  9. Cloud Platforms
  10. Familiarity with Google Cloud Platform (GCP).
  11. Data Engineering: Proficiency in data preprocessing and feature engineering.
  12. Version Control: Experience with GitHub for version control.
  13. Data Science Practices: Skills in building models, testing/validation, and deployment.
  14. Collaboration
  15. Experience working in an Agile framework.
  16. RAG Architecture:
  17. Experience with data ingestion, data retrieval, and data generation using optimal methods such as hybrid search.
  18. Google Cloud Platform:

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