1 day ago
London, United KingdomSenior
Responsibilities
- Implement ML workloads and MLOps capabilities on the Global AI Platform.
- Configure and optimize Databricks-based ML development and deployment frameworks.
- Implement model training, deployment, monitoring, and governance capabilities.
- Adapt or develop frameworks to support migrated workloads.
- Build CI/CD pipelines and automation for ML lifecycle management.
- Lead technical implementation of pilot models on the Global AI Platform.
- Develop reusable migration accelerators and standards.
Requirements
- Experienced Cloud MLOps Engineer with strong banking-domain expertise.
- Expertise in the Databricks Lakehouse Platform, Databricks ML, MLflow, Unity Catalog, Workflows, and Model Serving.
- Knowledge of CI/CD and MLOps automation.
- Proficiency in Python and production ML deployment patterns.
Benefits
- Hybrid work across company offices, client sites, and home, with no fully remote arrangement.
- Wellbeing support including Mental Health Champions and access to Thrive and Peppy wellbeing apps.
- Training and development opportunities, including thinktanks, hackathons, up to 250,000 courses, and external certifications.
- Access to learning and certifications from AWS, Microsoft, Harvard ManageMentor, and cybersecurity programs.
- Inclusive recruitment support through Capgemini’s Disability Confident Employer Level 2 commitment.
Categories
About Capgemini
Capgemini is a global IT services and consulting firm that delivers strategy, cloud, AI, software engineering, and managed services to large enterprises and public-sector clients. Founded in 1967 and headquartered in Paris, it is publicly traded on Euronext Paris and operates in 50+ countries. The group expanded its engineering capabilities by acquiring Altran in 2020, now operating as Capgemini Engineering.