About this role
About the Role Capgemini Japan is seeking an experienced AI Engineer to join our Insights and Data team. This role sits at the intersection of a large-scale insurance data ecosystem and advanced AI-driven intelligence. You will design, build, and deploy scalable Generative AI and Predictive Analytics solutions that transform our Azure-based data lake into actionable insights and automated, intelligent experiences. You will work closely with data engineers, platform teams, and business stakeholders to operationalize AI at enterprise scale. What You'll Do
- Design, train, and deploy predictive machine learning models and LLM-powered applications, including Retrieval-Augmented Generation (RAG) systems; leverage Azure Machine Learning and Azure AI Foundry to build scalable and production-ready AI solutions.
- Partner with data engineering teams to ingest and process high-volume datasets (Parquet, CSV, text) from Azure Data Lake Storage (ADLS) and Azure Synapse Analytics; ensure seamless integration between data pipelines and AI workflows.
- Develop serverless orchestration layers using Azure Functions to connect AI models with downstream applications and APIs; support real-time and batch inference use cases.
- Use Azure AI Search to enable low-latency retrieval for AI outputs, embeddings, and metadata; contribute to efficient data and metadata storage strategies.
- Implement MLOps best practices, including model versioning, monitoring, and lifecycle management; integrate AI workflows into CI/CD pipelines to enable automated testing and deployment. What We're Looking For
- Hands-on experience with Azure Machine Learning, Azure AI Foundry, and Azure OpenAI Service.
- Strong understanding of Azure Data Lake Storage (ADLS) and Azure Synapse Analytics; experience with Parquet files and large-scale enterprise data environments.
- Proficiency in Python for AI/ML development; working knowledge of Scala and Apache Spark; familiarity with enterprise ETL platforms.
- Backend & Storage: experience building serverless solutions using Azure Functions; familiarity with Azure Cosmos DB or other NoSQL data stores.
- DevOps & Automation: knowledge of CI/CD tooling such as Azure DevOps or GitHub Actions to automate AI and ML workflows.