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 ROLEDevelop, fine-tune, and deploy Generative AI models using AWS services like Bedrock, SageMaker, and Lambda.
Work with LLMs, embeddings, transformers, and diffusion models for applications in NLP, image generation, and AI automation.
Optimize prompt engineering, fine-tuning, and Reinforcement Learning from Human Feedback (RLHF) techniques.
Build scalable MLOps pipelines for training and deploying GenAI models using SageMaker, ECS, and Kubernetes.
Process and manage large-scale datasets for AI training using AWS Glue, Athena, and Redshift.
Implement vector databases (Pinecone, Weaviate, FAISS, Amazon OpenSearch) for efficient retrieval-augmented generation (RAG) applications.
Design and optimize ETL pipelines for AI/ML data workflows.
Collaborate with software engineers, DevOps, and product teams to integrate AI models into applications and APIs.
Ensure security, compliance, and data privacy in AI/ML workflows.
Monitor AI model performance and retraining needs using AWS CloudWatch, MLFlow, and other observability tools.
Strong background in Data Science, Machine Learning, and Generative AI.
Proficiency in Python, SQL, and ML frameworks (TensorFlow, PyTorch, Hugging Face Transformers).
Experience with AWS AI/ML services such as SageMaker, Bedrock, Lambda, and Comprehend.
Hands-on experience with LLMs, embeddings, transformers, and diffusion models.
Familiarity with Retrieval-Augmented Generation (RAG), vector databases, and knowledge graphs.
Experience in MLOps, containerization (Docker, Kubernetes, ECS), and CI/CD for ML pipelines.
Solid understanding of cloud optimization, distributed computing, and model scaling.
Strong data engineering skills for processing large datasets in AWS Glue, Athena, or Spark.
Knowledge of NLP, image generation models, or multimodal AI solutions.
Nice to have:
Experience with fine-tuning open-source models (LLaMA, Falcon, Mistral, Stable Diffusion).
AWS certifications such as AWS Certified Machine Learning – Specialty.
Experience with real-time AI applications, chatbot development, or autonomous agents.
Knowledge of ethical AI, bias mitigation, and AI safety best practices.
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.
Apply now!