Hybrid Cloud Data & AI Engineer for Insurance

Capgemini; Greater London; Hybrid; GBP 70,000 - 110,000; Full time

Benefits offered by this job

Wellbeing program

Learning for life

Job summary

Capgemini is seeking a highly skilled Cloud Data & AI Engineer to design, build, and deploy scalable data platforms and AI solutions on cloud environments, including Generative AI and LLM use cases.

The role requires close collaboration with business, data, and technology teams to deliver secure, reliable data products that drive business value, and to implement MLOps with MLflow for experiment tracking, model registry, versioning, and deployment.

Qualifications

• Hands-on experience in Cloud Data Engineering including designing and implementing scalable data platforms on cloud environments with expertise in leveraging and optimizing LLM-based use cases.
• Strong understanding of Insurance domain data models business processes and data governance requirements.
• Experience with MLflow for machine learning lifecycle management including experiment tracking model registry and deployment.
• Proficiency in unit testing version control Git CICD pipelines and production deployment of cloud based data and AI solutions ensuring reliability scalability and maintainability.

Responsibilities

• Design, build, and maintain scalable, secure, and high-performance 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 applications.
• Integrate AI services into cloud-native data platforms.
• Utilize MLflow for experiment tracking, model registry, versioning, and deployment.
• Establish and maintain MLOps practices for model governance and reproducibility.

Skills

Cloud Data Engineering

MLOps

LLM usage

Data governance

Insurance domain knowledge

Tools

MLflow

Git

CI/CD

Job description

Capgemini is seeking a highly skilled Cloud Data & AI Engineer to design, build, and deploy scalable data platforms and AI solutions on cloud environments, including Generative AI and LLM use cases.

The role requires close collaboration with business, data, and technology teams to deliver secure, reliable data products that drive business value, and to implement MLOps with MLflow for experiment tracking, model registry, versioning, and deployment.