Join Capgemini Engineering, a global leader in engineering services, and contribute to a team that empowers innovative companies to realize their full potential. We specialize in digital and software technology, driving innovation from autonomous vehicles to life-saving robotics across diverse industries. This role offers a dynamic career filled with opportunities for growth and impactful work, where no two days are the same.

Capgemini is a premier global partner in business and technology transformation. We assist organizations in accelerating their digital and sustainable initiatives, delivering measurable impact for businesses and society. As a responsible and diverse group of 340,000 professionals across over 50 countries, we leverage our 55-year heritage to unlock technology's value for our clients. Our end-to-end services span strategy, design, and engineering, amplified by leading capabilities in AI, generative AI, cloud, and data, complemented by deep industry knowledge and a robust partner ecosystem.

Design and implement scalable, end-to-end cloud-native AI/ML solution architectures tailored to client business needs. Lead architecture workshops, conduct technical deep-dives, and manage AI/ML proof-of-concept (PoC) implementations. Develop and maintain critical architecture artifacts, including High-Level Designs (HLDs), Low-Level Designs (LLDs), data flow diagrams, and Architecture Decision Records (ADRs). Provide technical leadership, mentorship, and quality assurance to AI engineering and delivery teams. Support pre-sales efforts through solutioning, proposal responses, effort estimations, and strategic technology recommendations.

This role requires 12-14 years of dedicated experience in AI/ML solution architecture, with a strong focus on cloud platforms like Azure OpenAI, AWS SageMaker, or Google Vertex AI. Proficiency in Python, SQL, and core ML frameworks such as TensorFlow, PyTorch, and Scikit-learn is essential. Demonstrable expertise in MLOps, model deployment strategies, MLflow/Kubeflow, feature stores, and robust data engineering pipelines is expected. A solid understanding of system design principles, data architecture, API design, containerization (Docker, Kubernetes), and cloud-native architectures is crucial. The ability to address critical aspects of security, scalability, performance, and cost optimization, while simultaneously providing technical guidance and engaging effectively with stakeholders, is vital for success in this position.