20 days ago
Responsibilities
- Architect scalable, secure, and modular generative AI solutions across enterprise and embedded ecosystems.
- Design and develop reusable AI assets, accelerators, and proof-of-concepts for industrialized project deployments.
- Define technology roadmaps and evaluate emerging AI tools, frameworks, foundation models, and AI services.
- Design and implement agentic AI architectures, autonomous workflows, and orchestration frameworks.
- Develop and optimize RAG solutions using vector databases and enterprise knowledge sources.
- Architect microservices-based AI platforms and integrations across cloud and enterprise systems.
- Support prompt orchestration, model integration, fine-tuning, observability, evaluation, and toolchain automation.
- Collaborate with business, delivery, engineering, data science, and AI teams to operationalize AI solutions.
- Establish governance, compliance, security, and monitoring frameworks for AI deployments.
Requirements
- 12+ years of experience in software architecture, AI/ML, or digital engineering with significant exposure to generative AI solutions.
- Strong expertise designing and implementing GenAI architectures and AI solution platforms.
- Hands-on experience with agentic AI frameworks, intelligent workflow orchestration, and autonomous or multi-agent systems.
- Strong understanding and implementation experience with RAG architectures, vector databases, and LLM integration workflows.
- Experience integrating AI services into enterprise and embedded products.
- Strong experience with microservices architecture, distributed systems, API management, enterprise integration patterns, and data flows.
- Proficiency in Python, Rust, or .NET development.
- Expertise with Azure and AWS cloud platforms.
- Hands-on experience with LangChain, Semantic Kernel, LlamaIndex, or similar AI orchestration frameworks.
- Experience implementing observability, monitoring, governance, compliance, and security for AI applications.
- Understanding of NLP, large language models, and foundation models, including OpenAI, Azure OpenAI, Amazon Bedrock, or Claude.
- Familiarity with model fine-tuning, prompt engineering, model evaluation, MLOps, LLMOps, and AI lifecycle management.
- Preferred experience with Kubernetes, Docker, CI/CD pipelines, cloud-native architectures, responsible AI, regulatory compliance, and enterprise AI transformation programs.
- Strong leadership, solution architecture, stakeholder management, customer-facing, communication, presentation, collaboration, and decision-influencing skills.
About Capgemini
Capgemini is an AI-powered global business and technology transformation partner, delivering tangible business value. We imagine the future of organizations and make it real with AI, technology and people. With our strong heritage of nearly 60 years, we are a responsible and diverse group of 420,000 team members in more than 50 countries. We deliver end-to-end services and solutions with our deep industry expertise and strong partner ecosystem, leveraging our capabilities across strategy, technology, design, engineering and business operations. The Group reported 2025 global revenues of €22.5 billion. Make it real | www.capgemini.com