Senior Data Scientist - AI & ML Engineer

About Capgemini

Capgemini is a global business and technology transformation partner, helping organizations to accelerate their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and society. It is a responsible and diverse group of 340,000 team members in more than 50 countries. With its strong over 55-year heritage, Capgemini is trusted by its clients to unlock the value of technology to address the entire breadth of their business needs. It delivers end-to-end services and solutions leveraging strengths from strategy and design to engineering, all fueled by its market leading capabilities in AI, generative AI, cloud and data, combined with its deep industry expertise and partner ecosystem. The Group reported 2024 global revenues of €22.1 billion.

Job Description:

• Design and implement Agentic AI solutions and applications.

• Lead the end-to-end lifecycle of machine learning and data science delivery engagements, from data ingestion and feature engineering to model training, evaluation, and deployment.

• Monitor model performance and implement retraining strategies to ensure long-term accuracy and relevance.

• Architect and optimize scalable data pipelines and cloud-based infrastructures (Azure, AWS, GCP) to support ML workloads.

• Collaborate with data engineers to ensure robust data preprocessing, transformation, and storage strategies.

• Develop and maintain CI/CD pipelines tailored for ML workflows, including automated testing, model versioning, and deployment.

• Integrate monitoring and alerting systems to track model drift, data quality, and system performance.

• Work closely with cross-functional teams including data scientists, product managers, and business stakeholders to translate business needs into technical solutions.

• Mentor junior engineers and contribute to knowledge sharing and best practices across the team.

Job Requirements:

• 6-10 years of experience working with complex datasets and solving real-world business problems using AI

• Proven track record of deploying and maintaining ML models in production environments and delivering AI solution architecture.

• Strong proficiency in Python, data science and machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch) and agentic application development libraries (e.g. LangChain, LangGraph).

• Strong command of SQL and experience with large-scale data processing tools (e.g., Spark, Pandas).

• Deep understanding of cloud platforms (Azure, AWS, GCP) and their ML services.

• Skilled in MLOps practices including CI/CD, containerization (e.g. Docker), and orchestration (Kubernetes).