Hybrid Cloud Data AI Engineer — MLOps LLM Generative AI

Capgemini; Greater London; Hybrid; GBP 70,000 - 95,000; Full time

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Job summary

Capgemini is seeking a Cloud Data & AI Engineer to design, develop, and deploy scalable cloud-based data platforms and AI solutions within the Insurance domain. You will collaborate with business and technology teams to deliver reliable, secure data products and will work in a hybrid model spanning Capgemini offices, client sites, and remote work.

The role focuses on building data pipelines, optimizing AI/ML solutions including Generative AI and LLM-based applications, and establishing robust

Qualifications

• Hands-on experience in cloud data engineering and scalable data platforms on cloud environments.
• Strong understanding of Insurance domain data models and governance requirements.
• Experience with MLflow for tracking experiments, model registry and deployment.
• Proficiency with version control, CI/CD pipelines, and production deployment of cloud-based data and AI solutions.

Responsibilities

• Design, build, and maintain scalable, secure data platforms on cloud environments.
• Develop and optimize data pipelines for batch and real-time data processing.
• Develop, deploy, and optimize AI/ML solutions including Generative AI and LLM-based use cases.
• Integrate AI services into cloud-native data platforms and establish MLOps practices.

Skills

Cloud Data Engineering

LLM-based use cases

MLOps

AI/ML lifecycle

Unit testing

Git

CI/CD pipelines

Production deployment

Insurance domain data

Model governance

Tools

MLflow

Git

CI/CD tooling

Job description

Capgemini is seeking a Cloud Data & AI Engineer to design, develop, and deploy scalable cloud-based data platforms and AI solutions within the Insurance domain. You will collaborate with business and technology teams to deliver reliable, secure data products and will work in a hybrid model spanning Capgemini offices, client sites, and remote work.

The role focuses on building data pipelines, optimizing AI/ML solutions including Generative AI and LLM-based applications, and establishing robust