AI Engineer

Description Indra is the central program driving the introduction and safe scaling of AI at DB. Focus is identify AI potential across various banking operations, driving funded use cases into production to create value and confidence and scale across the bank, creating selected shared services with embedded safety to enable low cost scale, developing an AI Workbench for developers for safe AI development at pace, and introducing AI controls whilst aiming to maintain time to market. What we’ll offer you As part of our flexible scheme, here are just some of the benefits that you’ll enjoy Best in class leave policy Gender neutral parental leaves 100% reimbursement under childcare assistance benefit (gender neutral) Sponsorship for Industry relevant certifications and education Employee Assistance Program for you and your family members Comprehensive Hospitalization Insurance for you and your dependents Accident and Term life Insurance Complementary Health screening for 35 yrs. and above Your key responsibilities Model Deployment:  Collaborate with data scientists to deploy machine learning models into production environments. Implement deployment strategies such as A/B testing or canary releases to ensure safe and controlled rollouts. Infrastructure Management:  Design and manage the infrastructure required for hosting ML models, including cloud resources and on-premises servers. Utilize containerization technologies like Docker to package models and dependencies.Continuous Integration/Continuous Deployment (CI/CD): Develop and maintain CI/CD pipelines for automating the testing, integration, and deployment of ML models. Implement version control to track changes in both code and model artifacts.Monitoring and Logging: Establish monitoring solutions to track the performance and health of deployed models. Set up logging mechanisms to capture relevant information for debugging and auditing purposes. Scalability and Resource Optimization:  Optimize ML infrastructure for scalability and cost-effectiveness. Implement auto-scaling mechanisms to handle varying workloads efficiently. Security and Compliance:  Enforce security best practices to safeguard both the models and the data they process. Ensure compliance with industry regulations and data protection standards. Data Management:  Oversee the management of data pipelines and data storage systems required for model training and inference. Implement data versioning and lineage tracking to maintain data integrity. Collaboration with Cross-Functional Teams:  Work closely with data scientists, software engineers, and other stakeholders to understand model requirements and system constraints. Collaborate with DevOps teams to align MLOps practices with broader organizational goals. Performance Optimization:  Continuously optimize and fine-tune ML models for better performance. Identify and address bottlenecks in the system to enhance overall efficiency. Documentation:  Maintain clear and comprehensive documentation of MLOps processes, infrastructure, and model deployment procedures. Document best practices and troubleshooting guides for the team. Your skills and experience University degree in a technical or quantitative field (e.g., computer science, mathematics, physics, economics, etc.), preferably a Master’s or Doctoral degree 2-4 years of experience in applying AI, machine learning and/or data science in business and/or academia. Strong knowledge of at least one programming language (e.g., Python, JavaScript) and relevant data science or engineering framework (e.g., scikit-learn, TensorFlow, Spark, etc.). Ideally, practical experience in finance and banking Comfortable working with and managing uncertainty and ambiguity Excellent oral and written communication skills in English How we’ll support you Training and development to help you excel in your career Coaching and support from experts in your team A culture of continuous learning to aid progression A range of flexible benefits that you can tailor to suit your needs