Data scientist

Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you’d like, where you’ll be supported and inspired by a collaborative community of colleagues around the world, and where you’ll be able to reimagine what’s possible. Join us and help the world’s leading organizations unlock the value of technology and build a more sustainable, more inclusive world. 

Your Role

As a Data Engineer, you will design, build, and maintain enterprise data platforms that support AI-driven products and analytics initiatives. You will work closely with Data Scientists, Architects, AI Engineers, and business stakeholders to develop robust data pipelines, digital twin platforms, knowledge graphs, and AI-ready data architectures.

• Design and implement data source connectors for telemetry, topology, intent, ITSM, knowledge repositories, and enterprise applications.
• Build ingestion, validation, replay, and data contract frameworks for large-scale data onboarding.
• Develop ingestion and topology mapping capabilities to transform raw telemetry into incident-aware datasets.
• Design, develop, and maintain Network Digital Twin and topology graph platforms.
• Build and manage Knowledge Graphs, ontology frameworks, entity relationships, and semantic data models.
• Develop reusable Feature Engineering Frameworks and Feature Store capabilities for AI/ML applications.
• Enable data foundations for RAG, GraphRAG, Vector Databases, and AI Agent frameworks.
• Develop and optimize data pipelines for model training, deployment, monitoring, and lifecycle management.
• Implement metadata management, lineage tracking, observability, security, auditing, and governance frameworks.
• Support integrations with AI reasoning engines, recommendation systems, and agent orchestration platforms.
• Collaborate with cross-functional teams to deliver scalable, enterprise-grade AI and analytics solutions.

Your Profile

• Strong programming expertise in Python and SQL.
• Experience with JavaScript, React, or Angular.
• Strong knowledge of Data Modeling, ETL/ELT, and Data Architecture principles.
• Hands-on experience with Apache Spark, Spark Streaming, and Kafka.
• Experience building and managing Data Lakes.
• Strong experience with AWS services including: S3, Glue, Lambda, SageMaker
• Knowledge of Machine Learning, Deep Learning, Predictive Analytics, and Statistical Modeling.
• Experience with Generative AI technologies including: Large Language Models (LLMs), Prompt Engineering, Retrieval-Augmented Generation (RAG) , AI Agents ,Agentic AI Frameworks
• Expertise in: Relational Databases, Graph Databases , Vector Databases
• Experience building APIs, Data Pipelines, and Batch/Real-Time Data Processing frameworks.
• Strong understanding of: Data Quality , Metadata Management, Data Lineage, Data Governance
• AWS Glue and Azure Data Factory.
• SageMaker Pipelines.
• Experience with Digital Twin platforms and architectures.
• Knowledge of GraphRAG and advanced RAG solutions.
• MLOps and LLMOps implementation experience.
• DataOps practices and automation frameworks.
• Experience with Enterprise Knowledge Management Platforms.
• Strong analytical, debugging, and problem-solving skills.

What You'll Love About Working Here

• Opportunity to work on cutting-edge AI, GenAI, Knowledge Graph, and Digital Twin technologies.
• Exposure to enterprise-scale AI and analytics transformation initiatives.
• Collaborative environment with Data Scientists, AI Engineers, Architects, and domain experts.
• Continuous learning opportunities across Data Engineering, GenAI, MLOps, and Cloud technologies.
• Ability to build scalable AI data foundations that drive measurable business impact and innovation.
• Innovation-driven culture focused on engineering excellence and technical growth.