Data Scientist - AWS

CAPGEMINI ENGINEERING

At Capgemini Engineering, the world leader in engineering services, we bring together a global team of engineers, scientists, and architects to help the world’s most innovative companies unleash their potential. From autonomous cars to life-saving robots, our digital and software technology experts think outside the box as they provide unique R&D and engineering services across all industries. Join us for a career full of opportunities. Where you can make a difference. Where no two days are the same.

YOUR ROLE

• Support the development, deployment and optimization of Data Science, Machine Learning and Generative AI solutions.

• Design, build and maintain AI/ML models, ensuring performance, scalability and reliability across different business use cases.

• Develop and optimize data pipelines, feature engineering processes and large-scale data processing workflows.

• Implement and support MLOps practices, including model deployment, monitoring, CI/CD pipelines and lifecycle management.

• Develop and maintain Retrieval-Augmented Generation (RAG) solutions, vector databases and knowledge-driven AI applications.

• Support the implementation, fine-tuning and evaluation of Large Language Models (LLMs), transformer architectures and diffusion models.

• Collaborate with data engineers, software engineers, cloud architects and business stakeholders to deliver AI-driven solutions.

• Contribute to AI platform evolution, cloud optimization, model scaling and continuous improvement initiatives.

YOUR PROFILE

• Degree in Data Science, Computer Science, Artificial Intelligence, Engineering or a related field.

• Experience in Data Science, Machine Learning, Generative AI or AI Engineering roles.

• Proficiency in Python, SQL and Machine Learning frameworks such as TensorFlow, PyTorch and Hugging Face Transformers.

• Experience with AWS AI/ML services, including SageMaker, Bedrock, Lambda and Comprehend.

• Hands-on experience with Large Language Models (LLMs), embeddings, transformers and diffusion models.

• Experience with Retrieval-Augmented Generation (RAG), vector databases and knowledge graphs.

• Knowledge of MLOps, containerization technologies such as Docker, Kubernetes and ECS, and CI/CD practices for ML pipelines.

• Strong data engineering skills for processing large datasets using AWS Glue, Athena, Spark or similar technologies.

• Understanding of cloud optimization, distributed computing and model scaling principles.

• Knowledge of NLP, image generation models, multimodal AI solutions and Generative AI technologies.

• Experience with fine-tuning open-source models such as LLaMA, Falcon, Mistral and Stable Diffusion.

• Knowledge of ethical AI principles, bias mitigation and AI safety best practices.

• AWS Certified Machine Learning – Specialty certification is considered a plus.

• Experience with real-time AI applications, chatbot development or autonomous agent solutions is considered a plus.

WHAT YOU’LL LOVE ABOUT WORKING HERE

• Join a multicultural and inclusive team environment.

• Enjoy a supportive atmosphere promoting work life balance.

• Hybrid work.

• Your career growth is central to our mission. Our array of career growth programs and diverse professionals are crafted to support you in exploring a world of opportunities.

• Access valuable training and certifications in cutting edge technologies.

• Engage in exciting national and international projects.

• Health and life insurance.

• Referral program with bonuses for talent recommendations.

• Great office locations.

ABOUT CAPGEMINI

Capgemini is an AI-powered global business and technology transformation partner, delivering tangible business value. We imagine the future of organizations and make it real with AI, technology, and people. With our strong heritage of nearly 60 years, we are a responsible and diverse group of 420,000 team members in more than 50 countries. We deliver end-to-end services and solutions with our deep industry expertise and strong partner ecosystem, leveraging our capabilities across strategy, technology, design, engineering and business operations.