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# GenAI & Agentic AI Architect

- **Company:** Cognizant
- **Location:** Amsterdam, Netherlands
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Computing, Program Optimization, Python (Programming Language), Machine Learning, Performance Tuning, Tensorflow, Search Technologies, Pytorch, Large Language Models, Multi-Agent Systems, Prompt Engineering, Spring-boot, Generative AI, Build Management, Scikit Learn, Kubernetes, Performance Monitor, Machine Learning Operations, Virtual Agents, Serverless Computing, Docker, Microservices
- **Published:** June 6, 2026
- **Apply:** https://dejobs.org/x/x/16B31843D05B42648001277130E0ADD9/job/

## About the Role

* Minimum 8+ years of experience in a technical architecture role
 * Proven expertise in Azure and/or AWS cloud architectures
 * Strong hands-on experience with Docker and Kubernetes
 * Proficiency in Python or R with experience in TensorFlow, PyTorch, or scikit-learn
 * Deep knowledge of MLOps practices and model deployment on AWS SageMaker, Azure AI, or Google Vertex AI
 * Experience with multi-agent orchestration and RAG architectures
 * Solid understanding of prompt engineering, vector search, and LLM observability frameworks
 * Experience with Spring Boot and microservices architecture
 * Strong understanding of enterprise AI security, scalability, and governance principles

## Description

We are seeking a highly experienced GenAI & Agentic AI Architect with strong hands-on expertise to lead the design, development, and implementation of advanced AI solutions for our clients. This role requires a seasoned technical leader with over 8 years of architecture experience and deep knowledge of Azure and/or AWS cloud ecosystems, combined with strong capabilities in containerization, orchestration, observability, and modern AI frameworks., * Architect and develop scalable, high-performance Generative AI solutions on Azure and/or AWS platforms

 * Design, build, and manage containerized AI applications using Docker and orchestrate deployments via Kubernetes
 * Establish and maintain observability frameworks for LLMs, including performance monitoring, system health, and usage analytics
 * Develop and implement machine learning models using Python or R, leveraging frameworks such as TensorFlow, PyTorch, or scikit-learn
 * Build and deploy AI/ML solutions using cloud-native services such as AWS SageMaker, Azure AI, or Google Vertex AI
 * Design and implement multi-agent systems and Retrieval-Augmented Generation (RAG) architectures
 * Define and enforce standards for prompt engineering, vector search, and model orchestration
 * Build and manage enterprise-grade MLOps pipelines covering model training, fine-tuning, evaluation, deployment, and monitoring
 * Collaborate with cross-functional teams to integrate AI capabilities into enterprise products and platforms
 * Lead system optimization, performance tuning, and troubleshooting of AI-driven applications
 * Establish best practices for secure, scalable, and maintainable AI architectures
 * Mentor and guide AI engineering teams on emerging technologies and implementation best practices
 * Stay current with advancements in Generative AI, Agentic AI, and cloud technologies


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